State of the Map 2019

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Opening Session

Opening Session

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Baden-Württemberg’s Ministry of Transport aims at developing regional mobility data towards an open and interoperable framework for new mobility solutions - as part of a sustainable traffic system, realising efficiency, climate protection and living quality. On the way there, fair chances for innovation are key: Not only for better results but also for a the involvement of most different societal actors. Cross-thinking and open innovation are corner stones in such a process.

Baden-Württemberg’s Ministry of Transport aims at developing regional mobility data towards an open and interoperable framework for new mobility solutions - as part of a sustainable traffic system, realising efficiency, climate protection and living quality. On the way there, fair chances for innovation are key: Not only for better results but also for a the involvement of most different societal actors. Cross-thinking and open innovation are corner stones in such a process. The keynote speech develops the need for open innovation against the background of the concept of sustainable mobility. It also gives insight into participation in innovation projects and the use of Open Street Map as a sustainable and non-discriminatory digital basis for new mobility.

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Karen M. Sandler is the executive director of the Software Freedom Conservancy, which is the nonprofit home of dozens of free and open source software projects including Git, Etherpad, QEMU, Selenium and Inkscape (to name a few. She is known as a cyborg lawyer for her advocacy for free software as a life-or-death issue, particularly in relation to the software on medical devices. Prior to joining Conservancy, she was the executive director of the GNOME Foundation. Before that, she was the general counsel of the Software Freedom Law Center. Karen co-organizes Outreachy, the award-winning outreach program for people who are subject to systemic bias and underrepresented in tech. Karen is an adjunct Lecturer-In-Law at Columbia Law School and a Visiting Scholar at UC Santa Cruz. She is the recipient of the Free Software Foundation's 2017 Award for the Advancement of Free Software as well as an O'Reilly Open Source Award.

Karen M. Sandler is the executive director of the Software Freedom Conservancy, which is the nonprofit home of dozens of free and open source software projects including Git, Etherpad, QEMU, Selenium and Inkscape (to name a few. She is known as a cyborg lawyer for her advocacy for free software as a life-or-death issue, particularly in relation to the software on medical devices. Prior to joining Conservancy, she was the executive director of the GNOME Foundation. Before that, she was the general counsel of the Software Freedom Law Center. Karen co-organizes Outreachy, the award-winning outreach program for people who are subject to systemic bias and underrepresented in tech. Karen is an adjunct Lecturer-In-Law at Columbia Law School and a Visiting Scholar at UC Santa Cruz. She is the recipient of the Free Software Foundation's 2017 Award for the Advancement of Free Software as well as an O'Reilly Open Source Award.

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This session provides an entertaining yet informative introduction into everything you need to know about OpenStreetMap.

If you're new to OpenStreetMap, or maybe just know a little, then this session will fill you in on everything you need to know about OpenStreetMap. How did it start? How does it work? How can you contribute, and how can you use the data? What is this "Mapnik" think I keep hearing about and why do people always look at me funny when I say the license should be changed? OpenStreetMap is not just a database or a web site; it is a living and breathing project with a huge community and a 15-year history. Rather than just showing a sequence of slides, this session will be a tour de force presented by many different individuals, where entertaining dialogue is interspersed with hands-on short demonstrations and explanations. Attendees will leave this session with a good idea of how OSM works, of how they can contribute to OSM, and of how to use its wealth of data for making their own maps or just counting post boxes in their home town. But they will also see that OSM is very much about human beings. They will learn how to become part of the community of map makers, code writers, and general activists that make OSM into the great success it is today.

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The PT_Assistant plugin has been developed over the past 3 summers as a GSoC project. It will probably see further development this summer. During this workshop, you can learn how PT_Assistant can help you to map itineraries for public transport, cycling and hiking. Interactively if you bring a computer with Java installed on it.

Mapping public transport is considered as hard to do. Cycling itineraries aren' t entirely trivial either. PT_Assistant is meant to help to make mapping these more efficient. Firstly it visualises the itineraries, focusing on what matters for public transport, i.e. numbered stop sequences. highlighted ways, so the itinerary you are currently working on stands out. Secondly, warnings are reported in the validator about 'gaps', vehicles traveling against oneway traffic, or not heeding turn restrictions and several more that are next to impossible to achieve without some extra programming. Last summer a routing helper was added, which means a lot less manual splitting of ways. Also, if an itinerary is mapped once and (parts of it) are used by other lines, the helper will propose larger sequences of ways to add in one go. It is already possible to use PT_Assistant for mapping walking and cycling routes, but that functionality will be extended further this summer. The reason to integrate it in the same plugin, even if the underlying principles differ, is that it fixing the public transport lines, but leaving other route relations in a "broken" state is not very nice either. Last year a "wizard" was added to help set up your JOSM environenment with MapCSS styles, Overpass Queries, Oneclick settings and accompanying toolbar buttons. So, if you want to learn more about how it can help you map route relations, come and join us, preferably with a laptop with JOSM installed on it.

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OSGeo is all about Open Source Software, Open Data and Open Education. Learn about OSGeo and what builds up this great organization and find out how OpenStreetMap is used by OSGeo projects, how OpenStreetMap can use OSGeo projects and how the two OSGeo and OpenStreetMap are conneted.

The Open Source Geospatial Foundation (OSGeo) is a not-for-profit organization whose mission is to foster global adoption of open geospatial technology by being an inclusive software foundation devoted to an open philosophy and participatory community driven development. The foundation provides financial, organizational and legal support to the broader open source geospatial community. OSGeo vision: Empower everyone with open source geospatial OSGeo was founded in 2006 and is well established as a global organization with Local Chapters all over the world. OSGeo represents and supports more than 25 software projects (https://www.osgeo.org/projects/) like QGIS, PostGIS or GDAL. OSGeo organizes many events global and regional all over the world (https://www.osgeo.org/events/). On every event OpenStreetMap is a topic in some of the talks or workshops. The initiative GeoForAll (https://www.osgeo.org/initiatives/geo-for-all/) is an OSGeo Committee and has an Educational outreach and has the mission for making geospatial education and opportunities accessible to all. There is also the OSGeoLive project that covers more than 50 software projects, Open Data and also documentation and tutorials. OSGeoLive is a self-contained bootable DVD, USB thumb drive or Virtual Machine based on Lubuntu, that allows you to try a wide variety of open source geospatial software without installing anything. OpenStreetMap data and software are also included on OSGeoLive. OpenStreetMap data is involved in many OSGeo projects f.e. as sample data, sample service. There is also functionality available to handle OSM data f.e the variety of OSM specific plugins for QGIS or support for OSM in GDAL (https://www.gdal.org/drv_osm.html) or routing on OSM data with pgRouting. Learn about OSGeo and how OSGeo and OSM are connected and join the OSGeo community.

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How to use plain OpenData and Imagery, to train, an accurate Deep Learning model, able to detect inconsistencies in OSM dataset, to spot it and to extract features. And make it works at scale, with OpenSource solution, named: RoboSat.pink.

Deep Learning approaches already proves that they can be helpful for QA or MissingMap areas. RoboSat.pink as an efficient OpenSource Deep Learning toolbox dedicated to GeoSpatial Imagery, can definitely help to quickly compare two datasets, as OSM and a coverage Imagery, and do it at scale. And spot where differences are significant enough, to value, that a human give them a look. This talk will focus on: - How to create an accurate trained model, for buildings and roads detection, from plain OpenData, without the needs to spend to much for hand-labeling features. - How to generate predictions faster, to lower the IT hardware footprint as much as we can. Point here, is to allow that anyone with a recent gamer video card, already can play with this tools. For information, RoboSat.pink main characteristics: - Provides several command line tools, you can combine together to build your own workflow - Follows geospatial standards to ease interoperability and data preparation - OSM data loader (using PyOsmium) - Build-in cutting edge Computer Vision model and loss implementations (and allows to replace by your owns) - Support either RGB or multibands imagery (as multispectral) - Allows Data Fusion - Rich and efficient Data Augmentation abilities (using Albumentations) - Static Web-UI tools to easily display, hilight or select results - High performances

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Building forward from our work presented last year at State of the Map, we have created a system to assist mappers via machine learned models. In addition, we have built an automated ingestion framework for OSM data at scale that allows us to selectively update parts of the map instead of doing a full snapshot change.

Building forward from our work on __Mobius Logical Changesets__ (presented last year at SotM US 2018), we have created an __automated ingestion and integrity framework__ for OSM data that allows us to __selectively__ update parts of the map instead of doing a full snapshot change all at once. Decomposing a large set of changes in this way gives us the flexibility to __rapidly ingest__ our own additions to the map, focus on __geographical areas of importance__ to downstream products, and allows us to __quickly apply hotfixes__ whenever egregious problems do arise. With millions of tiny changes happening every week, we have created a system that is built on __per-feature approval and preprocessing__, that allows us to ingest changes at scale, while creating rules to __automatically process logical changesets and enforce integrity constraints (e.g. anti-vandalism, anti-profanity etc.).__ Due to the contextual nature of some of the changes in OpenStreetMap, the system combines Human Approval, necessary for highly visible features such as names of large administrative areas, with __Automated AI/ML-based approval__: for example, using __computer vision techniques__ to reconcile newly created features against __satellite imagery ground truth__, or applying __NLP techniques__ to determine whether other user-visible string changes are sensible and valid. These components are combined to create a __continuous ingest-validate-deploy cycle__ for OSM map data.

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How useful are map features automatically extracted from street-level images? Can they be trusted? These are some of the questions we tried to answer through community campaigns and student-led research in 2019. We will share some of these lessons and elicit a broader discussion on the methods that can be used to turn automatically extracted features into useful OpenStreetMap data.

In early 2019, Mapillary began generating point data representing map features that have been recognized and extracted from images, including benches, fire hydrants, bike racks, and post boxes. We worked with various Mapillary communities and users to test the value of this new form of data. In this presentation, we’ll explore two campaigns. The first was the ***#mapillary2osm*** campaign which made point data available to six communities (Antwerp, Austin, Ballerup, Kyiv, Melbourne, São Paulo) in GeoJSON files and then encouraged map edits to turn each point into nodes on OpenStreetMap. Each location was given a 25 km^2 focus area and encouraged to add these points with the hashtag ***#mapillary2osm***, with the results being shared on a public leaderboard. The second was a project led by undergraduate students from the University of Washington’s Department of Geography. This particular project focused on a suburb of Portland, Oregon with the goal of validating and verifying Mapillary data in order to enrich OpenStreetMap. This involved measuring the difference between the OpenStreetMap data before and after the project and augmenting with Mapillary point data. We’ll conclude by looking at the longer term possibilities of editing OpenStreetMap with data derived using computer vision.

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Deep learning methods for feature extraction using computer vision are giving concrete results. This talk provides an overview of feature detection from satellite imagery and how machine learning can provide a seamless mapping experience for mappers, allowing them to utilize their knowledge to enrich maps further.

Deep learning methods for feature extraction using computer vision are giving concrete results. This talk provides an overview of feature detection from satellite imagery and how machine learning can provide a seamless experience for mappers, allowing them to utilize their knowledge to enrich maps further. When using OpenStreetMap for disaster response and humanitarian action, time and data quality are critical. Automated tools around OpenStreetMap have revolutionized disaster response, allowing mappers to leverage their local knowledge to organize themselves in action and to contribute to highly relevant maps. Over the last year, Tasking Manager team at HOT conceptualized an initiative to test using data derived using machine learning models to improve task creation, quality of edits and overall experience for mappers. Within Tasking Manager Working Groups we are collaborating between different actors to test concepts directly applied to two countries: Tanzania and Uganda. Two tools in pilot phase to assist mappers are around 1) Task Creation and facilitation: identify areas that need most work and/ or need an experienced mapper based on task complexity and gap analysis 2) Assisted Mapping: leverage machines to do the heavy lifting around digitizing features, such as buildings and roads, one by one and allowing mappers to focus on their essential craft of mapping. In this talk the presenters will share their findings, learning and existing challenges with the technology.

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It’s been more than a year since we at Grab incorporated OpenStreetMap into our ecosystem and we have come a long way since then. This talk will focus on our learning journey, our tools, the challenges and our experiences working in the South East Asia region and the vibrant OpenStreetMap communities here.

In the past few years, Grab as the biggest ride-sharing provider in Southeast Asia, is one of the biggest users and contributors of OpenStreetMap (OSM) in this region. We have put a great amount of effort into building a healthy ecosystem around it. We now have a better understanding of OpenStreetMap, mapping within its infrastructure, and how communities form one of its strongest pillars. We are excited to share how Grab started from scratch and gradually built a unified framework to manage OSM data as well as, more importantly, the velocity and data richness Grab has contributed back to OSM. We have worked closely with the different corporates, partners and the communities in the South East Asia region to support, build, guide, and empower ourselves and the community in contributing to improving OpenStreetMap. It has been a great learning journey so far with its share of peaks and valleys. Through this talk, we’d want to share our challenges and learnings from these experiences with the global OpenStreetMap community: - Grab’s ecosystem of managing OSM is a crowdsourcing system powered by data analysis, AI techniques, and other organic components - These include manual moderation and on the ground feedback from our well-trained map operations officers who are in every country striving to make the map as accurate as possible with local knowledge - Our experience in collaborating with the vibrant South East Asian Communities and different community partners like HOTOSM, through different programs and initiatives leading to a stronger community in this region - Collaborating with imagery partners like OpenStreetCam and Mapillary by contributing and making imagery available for cities like Singapore and Chiang Mai - Working along with the other corporates in this region as well as representing SEA on the OpenStreetMap Foundation advisory board - We will also share interesting findings of OSM in Southeast Asia and also shed light on how Grab has driven OSM to evolve here.

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Not every data item in OpenStreetMap can have an immediate impact on the rendered map. But you can make every data item visible on a map and share the links or share the data, no matter how much the item is special interest. This is the purpose of Overpass API and Overpass Turbo.

Our aim is to create special interest maps like this one for [historic street lamps](http://osmstreetlight.bplaced.net/#16/51.2339/6.7829), this one for [vehicle restrictions](http://maxheight.bplaced.net/overpass/map.html?zoom=14&lat=49.4&lon=8.68&layers=B0000000TFFTFFFFFFFF), or simply [all restaurants by cuisine](https://overpass-turbo.eu/s/I6z). An introduction to the Overpass API, to Overpass Turbo and the query language will be given. The aims are that the participants are afterwards able to filter for one for more tags, variants of tags, one or more keys, geographical regions, and combinations of such criteria. The Overpass API also supports to count objects, to dump them to GeoJSON, to retreive them in QGIS, in uMap, or a couple of [other downstream tools](https://dev.overpass-api.de/overpass-doc/en/targets/index.html). I will try my best to advance to these topics as well, but questions on the more basic issues have precedence. Thus the advanced topics are expected to rather go to an additional informal session if there are people interested in that. Please ensure that your are familar with the concepts of tags, nodes, ways, and relations, or read [an introduction here](https://dev.overpass-api.de/overpass-doc/en/preface/osm_data_model.html). You will benefit best from the workshop if you bring your own device with browser, internet access, and sufficiently large screen to view a map.

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Have you wondered if a certain dataset can be used for OSM? What does it mean for a license to be “compatible” with OSM and ODbL? This talk will give pointers on where to look for information, common compatibility issues, and best practices for presenting a license to LWG for help review.

The talk would go over tips for mappers to help them think through potential license issues for imports. The tips would cover all the major steps for determining compatibility: identifying the data's license, check OSM-related history, identifying/resolving compatibility issues, and achieving community consensus. This talk will be useful for any mapper with compatibility questions or who is interested in importing open data into OSM. Outline: - Sources of information about the license - Website (page from which data is downloaded, about page, FAQ, TOS) - Accompanying file (e.g., README, LICENSE) - Terms, credits, etc. - Metadata - Information about licenses/dataset previously evaluated for use with OSM - Imports wiki page - License compatibility page - forums/lists - Common compatibility issues - Attribution - Use restrictions (e.g., noncommercial/personal/research) - Privacy - Sharealike - Revocability - Indemnity - Local laws - Things that are usually not a problem (disclaimer of warranty/accuracy, trademarks, governing law, ) - Making it easy for others to read a license - A note about translation - Link everything! - Include all information about the source - Where to obtain - Description of where/who the data is from - Include all information about the license - What the license is - Any info about permitted uses - Any prior OSM discussion - Flag common compatibility issues and why you think they are or are not issues - Quote the relevant sections of the license (include the section number!) - Explain your reasoning - If the license references other laws/regulations/terms, look them up and link to and quote from them.

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Lightning Talks

## Uses of native language in OSM S M Sawan Shariar

Native Language uses in OSM is very important for any country specially in developing country.

## SolidaryCityMap OpenDEM

The aim of this project is to visualize places where you can participate in city life without papers and / or money.

## OpenLitterMap - Open Data on Plastic Pollution with Blockchain Rewards (Littercoin) Seán Lynch

Open litter mapping is a new and largely unexplored field of Geographic Information Science. During this talk you will learn about OpenLitterMap, how to use it, get some ideas about making relationships between OSM and OLM data, and learn how the OLM data can be used to improve OSM.

## Map2endFGM Janet Chapman

Mapping to fight Female Genital Mutilation in Tanzania by training first time rural women to map their communities and report gender based violence as part of WomenConnect

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OSMBC is the Content Management System to produce weeklyOSM in recently nine languages

weeklyOSM is the sister project of the German OSM blog (also well known as Wochennotiz). The infrastructure of weeklyOSM was created within the Comenius project "My community and my history in OpenStreetMap" by an international team from five countries and is being consigned, little by little, into the hands of the community. The aim is to share OSM news in a range of languages in addition to German and English. OSMBC "theFive" wrote a dedicated content-management system: OSMBC (OSM Blog Collector) back in mid 2015, especially designed for the needs of Wochennotiz and weeklyOSM. This system has been in use since WN #272. OSMBC allows news articles to be "collected" by the weeklyOSM team. This process previously was done entirely by the German Wochennotiz team. Later English and Spanish translations of Wochennotiz were managed within the tool, but these days increasingly articles are collected in different languages and cross-translated in a flexible workflow enabled by the OSMBC software. Despite the name, it is not limited to blogs. The team also checks international mailing lists and other channels to include any news that is relevant. The system supports "markdown" formatted text, for each article, and groups them into categories. These are ultimately converted to HTML and put into the wordpress blog each week. OSMBC is free and open source software and available on github. OSMBC is continuously adapted to the needs of the editorial team. For some time now, a guest mode has been available, the use of which will initially be discussed. The aim of the workshop is to familiarize beginners with the system. In addition, useful functions that can be used for translation will be shown using concrete examples.

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The OSM-Community uses many different platforms and channels to discuss, organize and document internal issues. This talk provides an overview about the relevance and reach of these tools at local and international level and also locks into pros and cons of this strategy of communication in OSM.

Creating a world map with as much detail as possible requires a lot of communication at local and international level. There must be a place to discuss local mapping details and to organize mapping and hacking events. Additionally, a lot of international communication is needed in international OSM projects as well as in the OSMF and the Working Groups. Plus users and software developers also have their own discussion and documentation channels. For this purpose a lot of tools and platforms are established in OSM: Mailing lists, forums, wikis, websites, social media, etc. This variety of communication media is important and makes OpenStreetMap the colorful, open and diverse project it is today. But it also complicates the search on a particular topic and raises a number of questions: Where do I have to search to get up-to-date information? How can you distinguish between proposals, current and outdated content? What do you do if many sources contradict each other? How can knowledge best be passed on to subsequent OSM generations? This talk is intended to overview various communication platforms and their relevance and reach at local and international level. In addition, the advantages and disadvantages of the current strategy for communication and knowledge transfer will be discussed.

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The Indoor Map Creator is an easy-to-use open-source map editor for OSM indoor data. It's web-based with an intuitive user interface to make mapping indoors accessible and easy for everyone. Check it out and enjoy mapping buildings!

The demand for Indoor Navigation is increasing worldwide. Unlike outdoor GPS navigation, indoor navigation is still rare and expanding at a slow pace. That is in part due to the fragmented landscape for positioning technology and in part due to the absence of indoor maps. Successful mapping starts with good tools. That's why we have developed a tool for mappers to create and edit indoor maps easily. With OSMInEdit we aim to repeat the OSM success story for the indoor space. The foundation for the OSM indoor data model is already laid out. The “Simple Indoor Tagging” schema is the community consensus on how to represent indoor data in OSM. It allows tagging of indoor specific content and is actively used and supported by the community. Popular OSM editors like ID and JOSM lack essential features that are required to map buildings - you need, for example, specific styling to filter data level by level, specific indoor tags, and indoor specific validation rules. OSMInEdit is an easy-to-use use web-based map editor that solves these specific challenges. It offers, for example, “Simple Indoor Tagging” presets, specific indoor styling, the ability to import custom floor plans, display them in the background and an easy way to edit routing graphs.

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Over the last few months, we’ve been building an offline first field mapping tool for the OpenStreetMap ecosystem called Observe. Observe makes field surveying, and verification easy for mappers, and works on iOS and Android. My talk will cover how Observe integrates with mapping workflows and helps improve OSM data from the field. I'll also touch upon fundamentals of building Observe and our assumptions on field mapping.

Field verification is an important part of keeping OSM data accurate. So far, field mapping exercises are largely manual, cumbersome, or requires internet connectivity. OpenStreetMap has an active mobile editing ecosystem, but they don't offer the same editing experience as iD for beginners. Most often mapping campaigns need an equally good tool that allows edits from the field to verify existing data and improve data quality. Observe is a cross-platform, offline-first field mapping tool for OpenStreetMap, perhaps the first of its kind. Our primary goal was to build an application that makes field observation easy, and provide comparable experience to iD on Android and iOS — with some success. Observe focuses on browsing OSM data, and allows users to add new points, or verify existing information. The edits made offline are stored on the phone, and uploaded when the mapper goes online. Observe is a product of several iterations of user research and a couple of years of conceptualization. The talk will: - introduce motivations behind building Observe - look at some specific use cases in campaign management - share internals of building the application - discuss challenges around offline editing and our approach to conflict resolution - gather feedback and launch a developer edition of the application

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What is an OSMF working group, what does the membership working group in particular do, and how can you improve the experience of an OSMF membership.

The OSMF working groups are the main way in which the foundation supports OSM in specific areas. I am part of the membership working group since 2016. I will talk about working groups in general, what we do in the membership working group, and also talk about my experience in the latter. In the last year, we had quite a few new things going on like the fee-waiver program, we could improve our processes by using a support tracker and getting part of our procedures finally written down. And then there was the ever increasing wave of member sign ups before the elections, which this year raised the question of how we can protect the OSMF from being taken over by lots of new members from a particular interest group. There is still a lot of room for improvements of the experience for the OSMF members, and a few tasks that are still unresolved will be discussed in the last part of my presentation. And maybe you are interested in helping with one of them. We are all volunteers and it turns out some things need quite a bit of time and endurance to get done. We would be glad to get new participants to our group.

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Exploring the visualization of OSM data outside its digital form and how it can be applied to everyday object from book cover to wallpaper. This talk will discuss on how to elevate OSM data beyond maps, exploring its potential in data-as-decor on both public and private space. The talk will describe the tools and workflow on how to create these product as well as its strengths and weakness.

When people describe OSM data, usually it will revolve around its structures, type and geometry. And normally the data will be transform into maps, both in printed and digital format. Yet, we believe that it should not stay that way. Several efforts have been made to transform OSM data into everyday product, from simple things such as book cover, t-shirt design to wallpaper or wall paintings. Its open data license enables vast array of customization into product that some may consider as a work of art without constrained by cost and licensing. We will showcase what have been created by OSM community all around the world from OSM data in everyday objects and several products that we created ourselves for event merchandises (book cover, key chains, sticker, mouse pad, goody bag) and office decoration. For the product that we create we will explain the tools (software and hardware), options for media used and what is the workflow to create such product so that it will inspire the audience to create their own product for their area of interest as well as building geographic identity through design. We want to proliferate OSM data and transform its data beyond maps and explore its potential in non-conventional design.

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Lightning Talks

## OpenStreetMap intrinsic quality assessment using “Is OSM up-to-date?” web application Marco Minghini and Francesco Frassinelli

Approaches to analyze OpenStreetMap (OSM) intrinsically, i.e. based on the history of data, have become an established way to achieve a wide range of final goals, most importantly to study its quality. Started in 2017, “Is OSM up-to-date?” is an open source web application licensed under AGPL and mainly written in Python. It can be also run in the command line or inside a Docker container. The target beneficiaries of the software are OSM users and communities, who need to assess the quality of data in a given area to decide whether to use it or not as well as where mapping efforts should be best directed, and OSM researchers and scholars, who can use it as a tool to help in their OSM quality studies. The project has a dedicated wiki page (https://wiki.openstreetmap.org/wiki/Is_OSM_up-to-date), source code is hosted on GitHub (https://github.com/frafra/is-osm-uptodate) and a demo is available at https://is-osm-uptodate.frafra.eu. Using the OSM API, the web application generates history-based, quality-oriented visualizations of OSM nodes and ways having at least one tag for any rectangular user-selected region. Such visualizations (on top of a grayscale OSM basemap) are available for the following criteria: date of creation, date of last edit, number of versions, number of different contributors who edited that node or way, and frequency of update. When an OSM node or way is clicked, a popup shows its values for the previously mentioned criteria as well as the list of currently available tags and the links to the OSM iD editor (in edit mode on that node or way), history and details of that node or way (both linked to the OSM website). A number of additional features to further improve the analysis of quality is currently planned (see https://github.com/frafra/is-osm-uptodate/labels/enhancement).

## Deriviste - Click and Go Photo Mapping Christopher Beddow

There are many OSM editing tools, but only one that allows the user to click inside a Mapillary image to add new data to the map. Richard Fairhurst's Deriviste tool was a quick experiment that proved this was possible, but it's important to ask more questions. How useful is the tool? How accurate and precise is it? How can it be improved?

## Fifty states in 12 years and five minutes Minh Nguyễn

A whirlwind historical tour of the OpenStreetMap project in the United States.

## "MAPAbabae": Using OSM as a Tool for Mapping with Women and For Women Andi Tabinas, Arnalie Vicario

We would like to share how we, as part of the Social Welfare and Development agency of the Philippines (DSWD), have utilized OSM to have a collaborative and gender-inclusive approach of mapping points of interests catering to women.

## Reverse Geocoding Is Not Simple Ilya Zverev

Having seen a dozen of different geocoders, I did not expect to find myself writing another one. But here I am, tasked with making a reverse geocoder better than the industry-standard Nominatim. Turns out it is a fun and not so straightforward task. Let’s see what can go wrong.

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We create an individualized map based on OSM using uMap

uMap lets you create a map with OpenStreetMap layers and embed it in your site. All within a few minutes. It is powered by open source, WTFPL-licensed software. The workshop is mainly designed for the students of the EU project. Of course everybody very welcome. No previous knowledge is expected. Depending on the learning progress of the participants, we will also respond to the creation of a constantly updated map, which reads data from OverpassTurbo. Here we follow the RobJN manual "Creating an always up to date map" http://www.mappa-mercia.org/2014/09/creating-an-always-up-to-date-map.html

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Get to know the Board and the Working Groups. Together, we run the OSMF. Let's use the SotM to have a real face to face meeting. The public is invited to join the conversation too.

SotM is about the community meeting the community. But we don't always seek out everyone we would like to talk to. So let's use this space to make sure as many members of the working groups and the board get to know each other as people and not just online text. All working group and board members are invited on the stage and can discuss anything. The public can also ask questions. Feel free to suggest some agenda items to joost -at- osmfoundation.org

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Can you actually walk through or fly over a 3D scene of live OSM data using a VR headset - and if so, can this be done in a way that easily transforms to a view on a normal computer (or mobile) screen? The answers are "Yes" and "Yes", and this talk will show how this can be done with relatively simple code, and just inside a web browser!

Mixed Reality (XR), i.e. Virtual and Augmented Reality, opens up new possibilities for 3D visualizations of OpenStreetMap data. With WebXR and Mozilla's A-Frame library, it's really simple to create cross-device XR experiences running right in the browser with very little code, and scaling well from mobile via laptop/desktop to fully immersive VR headset experiences. VR Map is a demo that brings all that together and allows you to walk or "fly" through a virtual model of the real world courtesy of live OSM data from the Overpass API. The talk will describe WebXR as Mixed Reality APIs for the browser and A-Frame as Mozilla's library to make it really simple to build WebXR scenes. Then, it will dive into how those technologies were used together with live OSM data to create the VR Map demo and show how it enables people to move through virtual models built from that real-world data. This will include a dive into an actual live demo, and into the HTML+JavaScript code used to create this experience. Through all that, it hopefully will show an interesting new perspective on OpenStreetMap - and hopefully inspire developers to build similar experiences.

talk on conference website

CyclOSM is a new cycling render. It is free and aims at rendering new bicycle-specific features, in order to take into account the diversity of cycling practices.

CyclOSM is a new cycling render. It is free and aims at rendering more features than OpenCycleMap, in order to take into account the diversity of cycling practices. For urban bikers (bike commuting), we decided to render all available types of cycle tracks and lanes, in a lateralized manner, as well as key POIs such as bicycle parkings, parkings shared with motorbikes, specific bike features (elevators / ramps), speed limitations, and surfaces of the highways to avoid paving stones. For bicycle tourers, we also rendered bicycle touring routes as well as a set of essential POIs (emergency services, shelters, tourism, shops) as wel las elevation data to help you design your bike journey. Building on the excellent quality of the OpenStreetMap data in Paris, we are also in touch with some local bicycle organizations to gather feedback and help them have a quick and detailed view of the current state of the cycling road infrastructure in the city. As such, a particular emphasis has been put on well rendering the quality of the infrastructures (clearly differentiating bidirectional cycle ways, dedicated lanes versus shared with public transport, etc) as well as some new elements which were not previously rendered: bike boxes and bumps. Designing a map render for every cyclist is a challenging task: bike infrastructures are usually parcimonious and very tightly bound to the road network, which makes bike-first visualizations not as readable as wished. There are also a wide variety of cyclist profiles, each requiring slightly different emphasis. CyclOSM is our attempt at tackling this challenge. In this talk, we will briefly sum up the main rendered features as well as discuss the design decisions and the difficulties of bicycle-oriented rendering. This talk will also provide a basic "getting started" summary, to help you start contributing and hacking this new render. A demo is already available at https://cyclosm.github.io/cyclosm-cartocss-style/example.html (limited to a few French cities at the moment, soon extended). The CyclOSM render is also available on the MapOSMatic instance https://maposmatic.osm-baustelle.de/. All contributions (issues, enhancements ideas or actual code through pull requests) are welcome at https://github.com/cyclosm/cyclosm-cartocss-style/ !

talk on conference website

The Desktop GIS QGIS offers many ways to work with OpenStreetMap data. This presentation will give an overview on how you can use OpenStreetMap data with QGIS.

The Desktop GIS QGIS (https://qgis.org) is Open Source software and an OSGeo project (https://osgeo.org). QGIS offers many possibilities to work with different sort of data - vector, raster & services. You can visualize, style, edit and process data and your have a great variety of additional functionality via Python plugins. QGIS offers many ways to work with OpenStreetMap data. Most of the possibilities can be added via plugins. You can download OSM data directly - complete data or only a subset via the Overpass API. You can use a search module for OSM data. Load OSM data as OGC WMS or as background. You can create great 3D maps with your OSM data. You can use a routing plugin and offer a routing on your OSM data. You can create field papers and create great print layouts. You can analyze your data and process it. You can publish your OSM data as an OGC WMS or WFS with QGIS Server. QGIS is a great Desktop QGIS and even more. This presentation will give an overview on how you can use OpenStreetMap data with QGIS and will demonstrate it via use cases. QGIS is written in C++ and uses the Qt library. QGIS is a community project with many people from all over the world involved.

talk on conference website

Hikar is an OSM-based augmented reality navigation app for walkers and hikers, showing OSM ways overlaid on the camera feed of the device and, in the latest version, virtual signposts pointing the way to nearby points of interest. This technical talk will introduce Hikar, detail the many new developments since it was last presented at SOTM Birmingham 2013, and describe how it works in technical detail.

Augmented reality (AR) has gained a great deal of media attention in recent years, helped in no small measure by the massive mainstream success of Pokemon Go. However, AR has a great deal of potential for real-world applications too, particularly for outdoor users, which has only partly been realised to date. A number of geospatial AR apps have been available for some years, including Layar and Wikitude, primarily focusing on points of interest (POIs), but many are proprietary and closed-source. One potential but little-explored application of AR is to provide navigation tools for hikers and other outdoor users, by overlaying paths and hiking trails on the camera feed of the device. An early version of the app discussed here, Hikar, was presented at SOTM Birmingham 2013, with the footpaths and trails sourced from OSM; however this version was difficult to use and was restricted in coverage to England and Wales. After a hiatus, development on Hikar has restarted in the past year, partly inspired by increased mainstream interest in AR and helped a great deal by the availability of global Terrarium elevation tiles and increasingly-affordable web hosting. The app (available on Google Play) now covers the whole of Europe and shows not only OSM ways but also virtual signposts, showing the distance and direction to nearby OSM POIs at path junctions, helping people navigate in the field. Virtual signposts support Greek and Cyrillic as well as Roman alphabets. The talk will include an in-depth technical discussion on how the virtual signposts feature has been implemented. Problems and issues with the app as it stands (such as the realism of the path and signpost placement, and inaccuracies with GPS and mapping data) will be discussed, along with strategies for resolving those problems, and future work (which may have already started by SOTM) will be outlined, including the use of computer vision and surface detection to more accurately place the virtual data within the real world.

talk on conference website

This talk presents an initial inquiry into the relations between the Academic and Mapping communities in OpenStreetMap, based on a review of recent publications, interviews of colleagues, and self-reflection of the authors. By this, we aim to understand how and when research-community interactions come to be, what is their nature, and how can these be improved and made more productive for both sides.

OpenStreetMap (OSM) can be conceptualized in a multitude of ways: it may be seen as a database, as a platform, as a concept, as a community (or collection of communities), as a social practice, etc. The academic research on OpenStreetMap adopts and utilizes these different conceptualizations, creating various forms of inquiry. For example, quality-related inquiries can be linked to the data/platform perspectives, contributor behaviors are analyzed quantitatively and qualitatively from a more behavioral perspective, and social understandings of OSM are utilized in inquiries into the institutional and community dimensions of the project. Indicative of a more general issue in the relations between geo-information and socio-cultural contexts, these readings of OSM do not represent absolute truths, but rather they emerge from the specific personal, professional, and socio-cultural backgrounds of OSM researchers (OSM-R). Furthermore, they hold the potential to create an effect on the world and specifically on OSM and its communities. However, the extent and nature of these relations in OSM-R, and specifically relations between research and the OSM community (OSM-C) have not received much academic attention yet. This is despite such interactions existing, e.g. when research outputs are presented to the community, when OSM contributors (OSMappers) become researchers themselves and vice versa, or on other occasions. Efforts to establish and strengthen the interaction between OSM-R and OSM-C have already resulted into significant outputs, e.g. the creation of a dedicated ‘OSM science’ mailing list and the stable inclusion of an Academic Track into the annual State of the Map conference. In this talk, we make a step further in the exploration of this issue, with the objective of not only better understanding these interactions but also formalizing an agenda for future OSM-R endeavors. Specifically, we look at the interactions between OSMappers and research communities, analyzing how the two affect each other, what are the implications of these interactions for both the researchers and the community, and how could these be changed to enhance relations and make them more productive ones. While this issue can be studied from the perspectives of both OSM-R and OSM-C, we focus on an initial exploration of the former. For this purpose, we employ two techniques. First, we review OSM-R publications from recent years (2016-2019) and, in addition to classifying them according to the researchers’ background discipline and the topic, consider what type of conceptualizations of OSM are employed there, and whether and how interactions with the OSM-C are considered explicitly. We use this analysis to make an initial assessment of the state of the issue in the field and identify how specific topics/backgrounds affect the ways in which OSM is conceptualized in research. Second, we collect detailed records of experiences of OSM-R/OSM-C interactions via the self-reflections of the authors and interviews with colleagues. While far from representative of the entire field, these allow a deeper observation of the causal processes that lead to the adoption of certain perspectives and to the development (or lack) of OSM-R/OSM-C interactions. In such a way, we gain insights into how researchers that are also mappers manage their different community roles and sets of objectives, when interactions (if any) happen, what their nature is, who initiates them, who dominates them, and why these came to be that way. Furthermore, these reflections allow speculation on how things could have been done differently, which opportunities were missed, and what possibilities exist. Thus, the combination of a view of current research status with an understanding of processes and forward-looking thinking allow us to point towards possible steps and procedures OSM-R could consider in order to create an impact on OSM-C and to enhance research via an understanding of OSM as a community.

talk on conference website

In this talk we try give an overview on how to use GraphHopper to provide a more flexible routing (based on weather information, road class, road width, ...) and how this could be also used for visualization purposes or data analysis.

In its first version 0.1 the open source GraphHopper routing engine was able to store just the distance and the car speed and access for every road. Since then many things have changed and improved in version 0.12 and beyond more data can be stored even without knowing Java or GraphHopper internals, but still the storage of those properties is done efficiently. A world wide graph with some basic useful road properties like highway, toll, tunnel, bridge, ferry, width, height, surface, maxspeed and access fits into a routable graph of under 25GB, i.e. just 60% of the planet PBF. GraphHopper allows you to keep this either in memory if you need high speed or serve the graph from the hard drive (incl. cache) to keep the costs low or use your development laptop. Developers and data analysts are enabled to store more features while preserving fast data access. The advantage of the graph-based storage of GraphHopper over a usual database is that the road connectivity can be directly exploited. This is a work in progress and we'll show what is already possible to provide a more flexible routing, data analysis and in-browser visualization with Leaflet. E.g. for routing purposes so called "what if" scenarios can be done to show the impact of a bridge construction via isochrones or reachable areas and also potential problems for HGV vehicles can be outlined.

talk on conference website

Lightning Talks provided by Scholars

## Mapping Advocacy: Breastfeeding Stations in the Philippines Jen Alconis Ayco

A mom mapper wants to give back to her breastfeeding support group of fellow mothers by mapping all publicly accessible breastfeeding stations in the Philippines.

## Open Data for Disaster Governance in Nepal Pradip Khatiwada

The recent proliferation of technology and open mapping brings new opportuniies to create an enabling environment where local governments and citizens work together and strive for co-created solutions to disaster problems. YI-Lab’s project on an integrated Disaster Information Management System unlocks the possibilities to use OSM datasets into disaster governance and engage local governments to contribute and strengthen on those data to enhance disaster risk reduction and preparedness.

## MapLesotho Tshedy Letsie

#MapLesotho has led to the possibility of a national spatial strategy in Lesotho, So i will do the lightning talk on how OSM data is used on Spatial Planning in Lesotho.

## State of the Map - India Naveen Francis

The talk will be about the development of mapping in India. Current status Admin boundary mapping, Infra mapping (Road, Rail, Electricity), Protected Area Mapping and Hospital import efforts. I will also cover the efforts of creating 'Indic Language Maps' category

## OSM for Gambia(mapping the whole Gambia) Jariatou Jallow

## HOT and OSM in Tanzania, the ups and downs of crowd-sourced mapping Emmor Nile

This talk will review some of the successes and challenges of crowd-sourced mapping of OSM in rural Tanzania from 2015-2019 with Red Cross and the Tanzania Development Trust

## Community Cartographers Fernando Castro T

A short introduction to the methodology we have used at OSM Colombia to teach indigenous people and rural workers to use OSM tools to collaboratively map their communities.

## Using OpenStreetMap Building Footprints Data for Population Distribution Model: A Case Study in Cavite, Philippines Dinnah Feye H. Andal

Spatially accurate population distribution maps are essential for disaster risk assessment. Philippines has undergone rapid urbanization and population growth but existing spatial population distribution data across the country is still lacking, thus the dependence for exposure data on Volunteered Geographic Information (VGI) is high. In this study, we explored the use of OpenStreetMap (OSM) data, with the census data of Cavite, Philippines in 2015 from the Philippine Statistics Authority (PSA), in generating population distribution maps for Cavite. The results showed that OSM database can be used to produce population distribution model using GIS. However, using OSM data might have some limitations as completeness and positional accuracy of the data can vary across different study areas. Previous studies have found that the completeness of data in populated or urban areas are better compared to rural areas. The model can be best used when the quality of OSM data such as its completeness, consistency, accuracy, validity, and timeliness is high.

## Public Transport in OSM Porfirio Carrasco

In 2014 the OSM Nicaragua community started mapping Public Transport, now we are working on increasing the coverage of it mapping other cities, on this brief talk you will learn how the local community is managing the whole project to achieve the goals and how to get easily started on Mapping the Public Transport in your city.

## Rendering order in openstreetmap-carto Matthijs Melissen

Why are tunnels under squares invisible? Why do tunnels hide buildings? Why is the Eiffel Tower invisible? Questions like these we often receive as maintainers of openstreetmap-carto, the default stylesheet on openstreetmap.org . Rendering order turns out to be a quite complex topic. In this short talk, I will give a brief overview of the rationale behind some of the rendering order decisions in openstreetmap-carto.

## MapATI : OpenStreetMap as a tool to help fight against the islands of urban heat Abdoulaye Diallo OpenStreetMap is a mapping project that aims to build a free and geographical database of the world. Following the consequences of global warming that the world is facing today through natural disasters, OSM, in addition to being a database development project, is also a tool for environmental awareness and management. By mapping urban areas (buildings, roads, points of interest) and rural or peri-urban areas where vegetation is important, this project can also be a tool to combat urban heat islands. This is done through the MapATI project, which means mapping trees or green space.

talk on conference website

Workforce Development and YouthMappers: Understanding perceptions of students in humanitarian mapping.

Patricia Solís and Sushil Rajagopalan The study tried to understand how and to what extent particular extracurricular (informal) activities through YouthMappers could impact workforce preparation and perceptions among students globally engaged in humanitarian mapping. [full abstract - pdf](https://zenodo.org/record/3387717#.XX_ckCgzZPY)

“Ohsome” OpenStreetMap Data Investigation for Cities in Brazil and Germany

Carolin Klonner, Maximilian Hartman, Lily Djam and Alexander Zipf For an adequate use of OpenStreetMap data, its quality needs to be known beforehand. We developed a tool, which bundles different intrinsic quality-related analyses, generates visualizations, and provides CSV files of the results. It is possible to perform analyses on multiple bounding boxes for the comparison of different regions. In contrast to many other approaches, this tool does not require a local database to work on, but accesses the OpenStreetMap history data via the ohsome-API. [full abstract - pdf](https://zenodo.org/record/3387725#.XX_cdCgzZPY)

Contextualizing OpenStreetMap in Mapping Favelas in Brazil

Everton Bortolini and Silvana Philippi Favelas in Brazil are spaces with unique characteristics and need to be mapped. Can OpenStreetMap be an alternative tool for this? [full-abstract - pdf](https://zenodo.org/record/3387727#.XX_cSSgzZPY)

How Knowing the Purpose of Mapping Changes the Map and the Mappers Themselves

Patricia Solís YouthMappers engages university students in humanitarian mapping which provides a potentially valuable learning experience beyond creating the open map. It may also pique new mappers’ interest, satisfaction, and confidence in spatial technologies as well as interest in the people and places that are served by humanitarian mapping projects. This presentation shares findings of a recently published study assessing how contextual information about the purpose of the humanitarian mapping task affects mapping and mappers themselves. [full abstract - pdf](https://zenodo.org/record/3387729#.XX_cESgzZPY)

OpenStreetMap as Space

Dipto Sarkar and So Hoi Kay Can OSM be used in classroom teaching as a definitive instance of a geographic space? We provide instances of how OSM as space may be of interest to researchers and for students of geography alike. Concepts of human geography manifested in OSM can be used to understand how digital geographies and offline activities are intrinsically interwoven with and influenced by each other. Thus, introduction to digital geography in classrooms can be through concepts in human geography. [full abstract - pdf](https://zenodo.org/record/3387699#.XX_czigzZPY)

Workforce Development and YouthMappers: Understanding perceptions of students in humanitarian mapping.

Patricia Solís and Sushil Rajagopalan The study tried to understand how and to what extent particular extracurricular (informal) activities through YouthMappers could impact workforce preparation and perceptions among students globally engaged in humanitarian mapping. [full abstract - pdf](https://zenodo.org/record/3387717#.XX_ckCgzZPY)

“Ohsome” OpenStreetMap Data Investigation for Cities in Brazil and Germany

Carolin Klonner, Maximilian Hartman, Lily Djam and Alexander Zipf For an adequate use of OpenStreetMap data, its quality needs to be known beforehand. We developed a tool, which bundles different intrinsic quality-related analyses, generates visualizations, and provides CSV files of the results. It is possible to perform analyses on multiple bounding boxes for the comparison of different regions. In contrast to many other approaches, this tool does not require a local database to work on, but accesses the OpenStreetMap history data via the ohsome-API. [full abstract - pdf](https://zenodo.org/record/3387725#.XX_cdCgzZPY)

Contextualizing OpenStreetMap in Mapping Favelas in Brazil

Everton Bortolini and Silvana Philippi Favelas in Brazil are spaces with unique characteristics and need to be mapped. Can OpenStreetMap be an alternative tool for this? [full-abstract - pdf](https://zenodo.org/record/3387727#.XX_cSSgzZPY)

How Knowing the Purpose of Mapping Changes the Map and the Mappers Themselves

Patricia Solís YouthMappers engages university students in humanitarian mapping which provides a potentially valuable learning experience beyond creating the open map. It may also pique new mappers’ interest, satisfaction, and confidence in spatial technologies as well as interest in the people and places that are served by humanitarian mapping projects. This presentation shares findings of a recently published study assessing how contextual information about the purpose of the humanitarian mapping task affects mapping and mappers themselves. [full abstract - pdf](https://zenodo.org/record/3387729#.XX_cESgzZPY)

OpenStreetMap as Space

Dipto Sarkar and So Hoi Kay Can OSM be used in classroom teaching as a definitive instance of a geographic space? We provide instances of how OSM as space may be of interest to researchers and for students of geography alike. Concepts of human geography manifested in OSM can be used to understand how digital geographies and offline activities are intrinsically interwoven with and influenced by each other. Thus, introduction to digital geography in classrooms can be through concepts in human geography. [full abstract - pdf](https://zenodo.org/record/3387699#.XX_czigzZPY)

talk on conference website

Who benefits from the mapathons: between (over)communication and (over)attribution, critical feedback on the inflation of a form of action oversold in the field of humanitarian action and development aid. Really for the benefit of OSM?

It would be possible to multiply the subtitles for this "Mapathon, mapathon, mapathon!" talk which questions the almost mandatory use taken by this form of collective OSM contribution for humanitarian and development aid stakeholders in the southern territories. "Mapathons in the "south" for what?", "A mapathon does not make a summer", or "Who benefits from mapathons?"... This presentation will aim to open a discussion about the inflation of one specific OSM contribution, to identify the diversity of its practices, the logics of communication, attribution, the quality issues of the data produced, and list the necessary conditions for a mapathon to actually benefit the OSM project in the southern countries and not harming its database. Like in Milano's Bird of Feather sessions last year, this talk will be collectively built with African mappers involved for many years in these activities and ideally co-facilitated by those from Western Africa successful in getting visas and traveling to Heidelberg. This talk is meant to introduce a Bird of Feather session opened to anyone active or interested in this topic and how it impacts the OSM dynamics in the hard environment of the countries of the Global South and especially southern countries of Africa.

talk on conference website

In this research, the OSM-Notes feature is mainly viewed as data that can be examined speedily from OSM data globally in terms of the content of the notes posted and the location of users.

Data from volunteered geographic information (VGI) and public surveying are essentially different; while a great quality and quantity of data is available for VGIs, discussions about data quality are scarce (Senaratne et al., 2017). As the use of VGI in business expands, attempts have been made to develop quality verification tools (cf. KeepRight and Validation OSM) with the understanding that, in addition to position accuracy, data quality equally depends upon the diversity and interaction of the number of users involved in data generation (Haklay et al, 2010). OSM-Notes is a capability for describing errors and discussing OSM data and fixme, although there are few research cases (Seto et al., 2017). This feature is new and was only added to OSM.org in April 2013. It allows users to specify and comment on any point on the map, and the history of comments can be accumulated and closed when the problem is solved. Unlike the fixme tag (allows contributors to mark objects and places that need further attention in the form of a "note to self" or request for additional mapping resources), this function does not directly associate with OSM data, but there is no need to have an OSM account. In this research, the OSM-Notes feature is mainly viewed as data that can be examined speedily from OSM data globally in terms of the content of the notes posted and the location of users. The purpose of the research is to analyze the context of OSM-Notes use through a “GIS” approach (Cope & Elwood, 2009). This analysis was conducted on data dumped from Planet OSM (https://planet.openstreetmap.org/) as April 2019. Since planet-notes.osn (about 782MB) is a special binary format, we used an enhanced parser able to separately output open (unresolved discussions) and closed (resolved discussions) based on osn2osm. After converting to .osm format data, the set was combined with Natural Earth's border data and population data and treated as spatial data. As a result, there were OSM-Notes postings in 237 countries: 415,433 open and 129,887 closed records. By counting the number of OSM-Notes postings by country based on whether they are open or closed, we determined that the majority were in the United States, Germany, and Russia. Moreover, it became clear that Japan, Canada, Korea, and Taiwan are the regions where there are a large number of submissions in urban areas (based on Natural Earth’s definition). In addition to these, many OSM users have posted from many countries, including Iraq, Ukraine, and Ecuador, and it is clear that active discussions are being held by many contributors. In these countries, mapping for humanitarian assistance is commonplace, as other reference resources are unavailable, so it is necessary to improve data quality through the use of OSM-Notes. OSM-Notes even has a function that allows non-OSM users to post, as is the case with 50% or more of the posts in Spain, Korea, etc. This data can be used to analyze urban trends and spatial features within a single country. For example, according to the analysis for Japan, OSM-Notes has many posts about the location of shops and POI (Point of Interest), and suggestions based on Maps.ME, Facebook, and Pokémon go. This is considered to be the main reason that anonymous posting is permitted. It is also worth noting that very few users post only a single note. Thus, by analyzing OSM-Notes, it is possible to grasp hot issues between users of OSM data. Overall, OSM mapping was found to be more common in developed countries with active mapping, but similarly, bug reports from non-OSM applications as developing countries that require discussion among OSM users, and as a new aspect. Moreover, how to accept it is an important issue in considering the process of constructing OSM data. However, because the functional relationship between OSM-Notes and the data on OSM cannot be specified directly, the nature of the feature is also difficult to grasp directly compared to the fixme tag.

talk on conference website

This talk will share lessons that OSM and OSMF members can learn about the multi-years collective dynamics around OpenStreetMap which unfold in French-speaking southern countries of Africa and the Caribbean with the view of identifying paths for local OSM grassroots groups evolution towards a formal OSMF local chapters.

It has been some years that OSM and OMSF members have been discussing the topic of OSMF local chapters over emails, wiki, physically through BoF sessions at SotM events. In 2019 the Ugandan NGO MapUganda applied to be the OSMF local chapter in this country. This talk will share lessons that OSM and OSMF members can learn about the multi-years collective dynamics around OpenStreetMap which unfold in French-speaking southern countries of Africa and the Caribbean. In these countries, since the start local OSM actions (outreach, communication, mapping and training) have been organized collectively. These actions lead to the de facto building of first informal collective of mappers which organized internally over the years and for some incorporated as local associations mandated to the promotion and support of the OSM project playing the role of a de facto OSM Chapter. The talk will look at the collective practices of these de facto OSMF local chapters in the light of the OSMF requirements but also look at the OSMF local chapters requirements in the light of this multi-years OSM associative experience with the view to identify itineraries to the building of OSMF local chapters. Like in Milano's BoF sessions, this talk will be collectively built with African mappers involved for many years in these activities and ideally co-facilitated by those from Western Africa successful in getting visas and traveling to Heidelberg. This talk is meant to introduce a BoF session opened to anyone active or interested in supporting the growth of OSMF local chapters connected to the pioneering grassroot collective in the hard environment of the countries of the Global South and especially southern countries of Africa.

talk on conference website

Satellite imagery has materially enhanced OpenStreetMap and improved editing and validation. In this talk we will talk about recent enhancements we've made to get even more information from satellite imagery.

DigitalGlobe, now known as Maxar Technologies, has been providing two sources of imagery directly for OpenStreetMap (OSM) users for tracing and validating. Over the past couple years, we’ve received a lot of excellent feedback from OSM users and we are making a few changes to better serve OSM volunteers. Part of this presentation will address some of the common questions we are asked, address some of the common feedback, and dive into the upcoming changes to the services that we provide. We want to do this to clear up any confusion or future questions the community might have. We will further demonstrate how we have integrated Maxar imagery into the OpenStreetMap ecosystem. In both iD and JOSM, we have developed a custom plugin that connects to the commercial satellite imagery service and provides a carousel view of available imagery in the visual extent of the editor. Images are arranged temporally providing opportunities to compare areas against different time periods. The plugin also applies the metadata from the image as tags on the feature, including source:imagery, source:imagery:layerName, source:imagery:sensor, and source:imagery:date. Leveraging these services and applying the metadata of the imagery enhances the mapping experience, enriches OSM data, and improves the end-user benefits.

talk on conference website

Lightning Talks

## Human in the Loop: Verifying Machine-Generated Data for Better Maps Said Turksever

Machine-generated map data has the potential to considerably accelerate mapping at scale. Combining it with human review helps ensure high data quality. We’ll show how a simple game-based tool helps verify the map data generated by Mapillary’s AI, and how that data helps enhance OpenStreetMap.

## Share the word Ilya Zverev

WeeklyOSM is great, but is it the only channel for following news in OpenStreetMap? For most countries, yes. And that is sad. You can change it for the better: start a blog, record a podcast, tweet something. Here I will share my experience at keeping OSM community informed.

## Enhancing OSM with missing roads Beata Tautan-Jancso

ImproveOSM is a powerful tool that detects and highlights areas in OSM where roads, one-way attributes, and turn restrictions are missing from the map. The first version of the JOSM plugin was released in 2015. Since then, the community has improved almost 200.000 areas with missing roads.

## Community led mapping helping in policy changes Sibabrata Choudhury

Beginning of 2015 a process of community consultation and community led advocacy in the eastern state of India has resulted in several communities developing maps of their plots which has been a breakthrough experience.

## How to create a data annotation process used for navigation Alina Negreanu

In this talk, the Telenav OpenTerra team will present how they built their data annotation team and the processes they developed in order to assure high-quality annotations, on which their AI algorithms heavily rely on. They will talk about good practices to use when building manual datasets and the hurdles they had to overcome in order to reach their quality requirements, having so far reached more than 600 000 annotations.

talk on conference website

The OpenStreetMap (OSM) community is a global community crossing cultures, languages, and geographical boundaries. Researchers have been working to develop automated approaches to understanding the composition of this community through their contributions to the OSM database. In this talk we propose a new and novel application of theories and models of species abundance from ecological science to understand contributor community structure and distributions in OSM.

**Motivation: ** Community is a word that evokes different images for different people. Socially, we as humans require interaction with other people and society is built around people coming together into social groups we call ‘community’. Communities identify different groups and very often the bond within these communities in a set of shared goals and the division of sharing of labour and skills among other resources. Indeed some scholars believe that the feeling of contributing positively to our own communities is one of the most fundamental feelings of satisfaction in life (Proctor, 2013). In all of these ways the now millions of contributors to OSM form the OSM Community. Attempts to understand how the OSM community works have appeared in the academic literature. Amongst the research community there is a curiosity and fascination about the OSM community given: the global extent of OSM crossing cultures, geographical boundaries and languages; the altruistic nature of its members; and its obvious success as a primarily Internet-based community different to almost every non-crowdsourced community we know from our everyday lives. **State of the Art: ** In this talk we shall argue that the model of community required for OSM is more nuanced that many of the current quantitative approaches. Neis et al (2013) amongst others have used concepts of junior, senior, local, external mappers which does capture the distribution of contributors to OSM well. OSM has been shown to loosely adhere to the 90-9-1 rule of Neilsen (2012) which highlights that about 90% of the members of community-based projects are usually only consuming the collaboratively collected information, while 9% occasionally contributes to the project and only 1% demonstrate a very active pattern of contribution of activity. As Begin (2018; PhD Thesis) argues 'characterizing Volunteered Geographic Information (VGI) data requires understanding contributors’ behaviour and many typologies of contributors are proposed in an attempt to link VGI contributors with the nature of the data they provide'. In Begin et al (2018) the authors identifies the different phases of contributor life cycle from a temporal perspective as a contributor's lifespan is a 'university metric'. In a more computationally complex approach Truong et al (2018) develop a multigraph approach with data mining to characterise individuals and identify behavioural groups. The implementation of a multiplex network based on an OSM data sample and an initial analysis make it possible to identify useful behaviours. **Methodology**: We consider a very novel approach to community identification and understanding by borrowing concepts and methodologies from theories and models of species abundance to the individual contributors of the OSM community. This is a novel approach in VGI but a decades old and mature branch of Ecological Science. As Hughes (1986) points out "It is a common observation that in samples from animal and plant communities most of the individuals belong to a small number of abundant species, whereas most of the species are represented by a small number of individuals". In OSM we see that most individual contributors make a small number of edits. However, from the global OSM community, a small number of species (groupings) are represented by a small number of contributors. For example contributors who have contributed thousands of GPS traces or thousands of building objects. We use the OSM Planet History data for a number of selected regions to consider the contribution history of those OSM community members who have contributed in those regions. All software is developed in Python. We then develop and apply the Community Level Modesl (CLMs) from Maguire et al (2016) and others. We define different types of OSM community member species. Species characteristics are based on contribution history and patterns and can be easily changed. For example, we may create a species which are differentiated by the number of OSM Relations they have created/edited. We could decide on three species groups: 0 - 10 relations, 10 - 100 relations or greater than 100. More sophisticated species can be developed. CLMs allow the creation of a species co-occurrence matrix to environmental variables (such as quantity of edits, types of tagging used, etc) which allows prediction of the community structure and the distributions of individual species. Maguire et al (2016) argue that in ecological communities CLMS have the potential to predict species distributions and changes in the community composition more accurately than other models such as species distribution models (SDM). Assuming that contributors to OSM exist in isolation and do not influence one another's distributions potentially limits our ability to understand the patterns of contribution in the community. The application of this innovative approach from Ecological Science means we can potentially better understand the interactions between contributors. This has the future potential for improved iterations between experienced contributors and new entrants and the organisation of local events such as mapping parties.

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The Tasking Manager is OpenStreetMap’s most used software tool to organize mapathons, community mapping initiatives and professional mapping teams. Over the last year it has been developed further significantly. This talk will give an update on the newest developments and the emerging community around the application.

The Tasking Manager is OpenStreetMap’s most used software tool to organize mapathons, community mapping initiatives and professional mapping teams. Over the last year it has been further developed significantly. This talk will give an update on the newest developments and the emerging community around the application. Based on tales of data storytelling representatives of the development team of the Tasking Manager are going to discover remarkable curiosities and insights out of a wider application and user analysis, of course backed by hard stats. From this starting point we explain the solutions we implemented in Tasking Manager in the last year to improve the user experience and increase the data quality by mappers using the tool. Three stories will be told, about group of users that are coming to the Tasking Manager frequently and how the changes affected their flow. New mappers, validators and project creators are the main actors of the exciting adventures of combating a low beginners retention rate, lifting data relicts, guiding a mapper through the labyrinth of OSM contribution and learning about the magic of people’s using technology to achieve what they want. We will talk about the new ingredients of the Tasking Manager, from the redesign of the users interface and the interaction among them, to how it now fits into a wider spectrum with much more applications of the OpenStreetMap ecosystem in order to improve direct access to more efficient mapping and data validation.

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The time OSM mappers invest in labeling the world is valuable. We present how methods from remote sensing, big data distributed computing and artificial intelligence can be combined to support human analysis of geo-spatial data.

The past decade has shown a dramatic increase in the amount of openly available geo-spatial datasets such as - multi-spectral and RADAR satellite imagery from space agencies like ESA and NASA - government-sponsored, high-resolution aerial survey raster data, e.g., USDA (U.S. Department of Agriculture) - weather reanalysis model data based on sensor networks, e.g. published by the PRISM Climate group - geo-tagged messages as well as images from social media platforms such as Twitter and Instagram, etc. accumulating geo-tagged information at data rates easily exceeding tens of terabytes a day**. Given that an open-source project such as OSM relies on volunteers to spend their valuable time*** to generate vector datasets that annotate and update information on roads, buildings, land cover, points of interest, etc., it is natural to ask how sources of freely available spatio-temporal information might help to support and guide mappers in their work. At the same time, major progress has been made in the "open-source digital arena" of big data processing and artificial intelligence (AI). For example, projects for distributed non-relational database systems such as HBase (https://hbase.apache.org/) or in-memory distributed compute frameworks such as Spark are available to run on commodity hardware to scale analytics. Deep learning libraries such as PyTorch (https://pytorch.org) in accordance with the explosive amount of neural network architectures published by academia enable, for example, state-of-the-art computer vision algorithms which can be leveraged for remote sensing tasks: detection of buildings, land classification, change detection, etc.. Our presentation will discuss and demonstrate how to link tools from big data analytics and machine learning to geo-spatial datasets at scale in order to extract value from openly available spatio-temporal datasets to the potential benefit of OSM mappers. In particular, we show the design of a system that employs the key-value store HBase to index spatio-temporal satellite imagery to let Spark-SQL (https://spark.apache.org/sql/) user-defined functions act on it to remotely identify human signatures on Earth's surface by the aid of AI. Finally, when it comes to pixel-wise land classification, we are using the 1-meter resolution USDA aerial survey data and information derived from the Open Street Map project. The goal is to establish a scalable pixel level translation model from aerial map to OSM, where colors and shapes define land classification, i.e., forestry, grassland, building, road, etc. The USDA aerial survey is refreshed every other year, so we expect to translate the latest aerial survey to OSM and compare with the current OSM state to identify changes on the actual land use. This information will guide the OSM community where the map needs to be updated. We believe that the techniques and use cases presented will help to identify "hot spots" of where OSM needs human labor most - either in mapping or updating labels. Moreover, we hope to spark a scientific, strategic and technical conversation with the OSM community on needs regarding semi-automated support systems for global mapping. If time permits, as a bonus, we will introduce the open-source tool https://github.com/IBM/ibmpairs to interact with the spatio-temporal platform PAIRS that supports our research. **Although precise numbers are hard to find in the literature, a rough estimate based on ESA’s Sentinel satellite imagery can be made: Approximately providing global coverage over land on 30 meters resolution in 10 spectral bands once a week, 150 million square kilometers of global land surface generate about 15*10^7*10*1000 pixels, i.e. we get rates of order of 6*10^12 bytes per week, assuming 4 byte floating-point numbers as information storage per band. Scanning the list of openly available data from NASA and ESA alone, it is fair to assume there exists at least a hand full of such products. Please note, typically there exists an inverse relation between spatial and temporal resolution of geo-spatial data products: The coarser the spatial resolution, the more often data is published. ***Beginning of 2018 the (un)compressed overall OSM XML history file was 0.1TB (2TB). 5 years before, the historical data had accumulated about 40GB compressed. Assuming an OSM mapper adds a new record/modification (nodes+way+tags) of order of tens of KB in minutes, the time-invested to real-time ratio reads [60*10^6KB*20/(20KB/60s)]/(5*365*24*60*60s) ~ 10^6/(4*10^4) ~ 25. I.e. an estimated 25 mappers have to work around the clock to generate OSM labels.

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This talk will present the rise of active, self-standing grassroots communities in Haiti, Western and Central Africa since 2010 resulting from a unique set of continued support actions replicable in other territories, by an ensemble of speakers from (at least) France, Burkina Faso, Senegal and Togo

This breakout session will produce a narrative about how since 2010, OpenStreetMap communities emerged in Haiti and expanded to Western and Central Africa after 2012 partly as the result of a combined set of support actions spanning technical and organizational OSM trainings, voluntary and professional OSM mapping projects, OSM local and international volunteering programs, documentation as well as preparedness and crisis mapping work... Tied to the overall support of the global OSM community and the commitment of Haitian and African mappers, these actions allowed the OSM project to come forth in the form of a network of organized and self-standing communities and economic stakeholders in Haiti, Western and Central Africa cognizant of and active with OpenStreetMap, Open data and free geomatics. Like in Milano's Bird of Feathers sessions last year, this talk will be collectively built with African mappers involved in these multi-years activities and ideally co-facilitated by those from Western Africa successful in getting visas and traveling to Heidelberg. This talk is meant to introduce a Bird of Feathers session opened to anyone active or interested in growing OSM the grassroots way in the hard environment of the countries of the Global South and especially southern countries of Africa. ### Moderation * Nicolas Chavent (France) * Séverin Ménard (France) * Amadou Ndong (Senegal) ### Speakers * Claire Halleux (Belgium, SotM DRC) * Jariatou Jallow (Gambia, Connected YMCA Gambia) * Trudy Hope (Zambia, Mapping for Wash in Zambia) * Fanevanjanahary Andriamiadantsoa (Madagascar, OSM MG community) * Nathalie Astou Sidibé (Mali, Mapping access to water in rural Mali) * Saliou Abdou (Benin, SotM Benin) Additional scholars from Africa may also join.

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During this session I’ll share some tips & tools for new comers to start making their first edits on OpenStreetMap. I’ll do so by explaining the mapping concepts, process, the use of editors, and will continue with the practicing part. By explaining this process , I aim to make it easier for new contributors to get started with editing and will show some daily life examples to illustrate the importance of even the smallest contributions.

During this session I will share some tips & tools for new comers on how they can start making their first edits on OpenStreetMap. I will do so by first explaining the mapping concepts (node, way, tag, area, editor etc), the process of mapping and some of the editors we can use to edit. I will continue after with the practising part, where I’ll demonstrate to participants how to make an improvement on the map. During the session we will go through the iD editor which is quite simple to use for beginners, and will mention some of the main features it has. By explaining the whole process step by step, I aim to make it as much easy as possible for new contributors to get started with editing OSM. I’ll briefly explain some of the formal rules we should consider when editing. I will also show some daily life examples which can illustrate that even the smallest contributions can be of great value for our cities and countries, no matter where we live, which hopefully will motivate the participants to start editing right away. At the end of the workshop I'll also share some learning resources which have been very valuable in my experience and for those who want to read and practice more, might be of great value as well.

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Services that provide money advice, access to food provision, digital access and community support are key to supporting those facing poverty. “Our Falkirk” is a simple mapping platform for service discovery that allows enriched OSM data to be easily described, mapped and shared through the concept of data ‘themes’.

Falkirk Council is one of 32 Local Authorities in Scotland. Located in the centre of Scotland with a population of around 155,000 people, 1 in 5 people and 1 in 4 children in the Falkirk Council area are estimated to be living in poverty. Fairer Falkirk is Falkirk Council’s strategic response to the rising poverty in the Falkirk Council area. It brings community planning partners together and sets out in detail a series of practical, deliverable, and achievable programmes aimed towards mitigating the impact of poverty on individuals and families. Services that provide money advice, access to food provision, digital access and community support are key to supporting those facing poverty, but ensuring that local people and front-line staff have access to up-to-date information relating to these services is challenging. With funding from the Open Data Institute (ODI)’s Local Government Geospatial Data Stimulus Fund, Fairer Falkirk and their technology partner thinkWhere have harnessed the richness and flexibility of OpenStreetMap to create “Our Falkirk”: an online, map-based tool to allow local people to easily access information on services in the area. The presentation will look at how we’ve created a simple mapping platform for service discovery that allows enriched OSM data to be easily described, mapped and shared through the concept of data ‘themes’. We’ll look at some of the key challenges and opportunities identified through this process, and show how taking an Open Data approach has allowed Falkirk Council to democratise access to vital information through the streamlining of data creation of publication. We’ll look at potential next steps and sign-post the resources now available as a result of this fully open–sourced project.

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We've learnt about many projects and clever systems that run OpenStreetMap possible, but it's the individuals that are most valuable in what we do. Settle down in your seats as Gregory introduces you to a selection of OSMers and informally chats to them about their involvement. When, how, and why did they join OSM? What do they do in the project? Has that changed? What's their favourite map tag? All these questions and more will potentially be asked.

We've learnt about many projects and clever systems that run OpenStreetMap possible, but it's the individuals that are most valuable in what we do. Settle down in your seats as Gregory introduces you to a selection of OSMers and informally chats to them about their involvement. When, how, and why did they join OSM? What do they do in the project? Has that changed? What's their favourite map tag? All these questions and more will potentially be asked. Come to the talk to find out which OpenStreetMappers we will be having a conversation with. Gregory held back from joining until he met some OSM guys in the UK in 2006. You may have seen him shout "Maps" on the stage, but he really enjoys chatting to people in the breaks. If it's your first time or you've got every SotM t-shirt that exists, do find him and say hello.

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Mapathons and Imports represent a Quality Challenge for the OSM community. This presentation focuses on Buildings. It presents Metrics and show progress of tools development to monitor and correct Quality problems in the OSM database or before importing.

Mapathons and Building import projects represent major OSM Quality problems to monitor. Potentiel 3.0 and OSM-RDC started a project in 2018 to monitor Data quality problems. We present the work done with metrics to monitor quality problems among various Projects (see [OpenDataLabRDC Blog Articles about Building Quality](https://opendatalabrdc.github.io/Blog/#!index_en.md) ). Individual building geometries are evaluated and classified (ie. orthogonal, quasi-orthogonal or irregular). Topological errors are detected (ie. incomplete polygon, self-crossing, overlaps). Geojson visualisations and Statistics let evaluate a project. Output of List of osm_id's for various problems (ie. topological error, irregular building) let's import simply into JOSM with Overpass queries. Current development is to go a step further and correct buildings quasi-orthogonal angles in an OSM file taking into account neighbor buildings to avoid distorsions with blocks of buildings aligned on a street (See [OQ_Analysis](https://github.com/pierzen/OQ_Analysis)). Programming is based on the Osmosis schema, Python and PostgreSQL-PostGIS. OSM files to analyse can be imported from various sources (ie. Geofabrik, Overpass, osm export). Each OSM file (specific date-Zone) is converted to PosGIS and stored in a specific PostgreSQL schema via Osmosis. The topological analysis and geometry measures are made with PostGIS functions.

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This talk discusses different ways how to improve the search experience for domain-specific maps.

No map comes without a facility to search for places. As a result quite a few open-source geocoders using OpenStreetMap data have been developed and have matured in the last years. Nominatim, Pelias, Photon, Carmen, there are many to choose from. They all have in common that they offer a general purpose search for addresses, places and, to a more limited extent, places of interest. OpenStreetMap has inspired the creation of many domain-specific maps. Be it maps for specific features like brew pubs, camping or power infrastructure or for activities like cycling, hiking or child entertainment. A general purpose search on these maps is often not satisfying. The special features might simply not be available in the generic geocoders. And when they exist, they do not get the prominence appropriate for the map. This talk explores ways to improve the search experience for such special-interest maps. We start with simple approaches to boost results using existing web APIs and then look into different ways for creating a small custom domain-specific search engine. We discuss advantages and disadvantages of the different approaches and show common pitfalls for implementations. Although some knowledge in system administration and programming is expected, the focus is on solutions for small and medium projects that are realized with limited resources.

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We propose a model that uses only open data for estimating the minimal energy use of individual buildings for heating and cooling at scale. The workflow is divided in two main blocks: (i) predicting at scale a 3D building stock using OpenStreetMap data, and (ii) estimating the energy use of buildings individually with a back-end model.

For mitigating climate change, it is crucial to minimize energy use in buildings while maintaining decent wellbeing (1). Buildings contribute more than a quarter of the global energy-related emissions (2). On the one hand, buildings are among the lowest-hanging fruit for mitigation—with technological solutions available for energy-neutral or even energy-positive buildings. On the other hand, deep decarbonization is challenging because of the heterogeneity in the building stock (2). Moreover, achieving decent well-being conditions for everyone could increase the energy consumption of buildings. For instance, in countries like India where global warming is likely is intensify the deadly effects of heatwaves, air-conditioning is considered an important adaptation strategy. Currently, building energy demand, e.g. for heating and cooling, is mostly estimated for individual buildings with data-intensive models, or with fudge factors in highly aggregated models globally. For rapid, wide-spread mitigation, such methods are not sufficient either to develop solutions that can be transferred across urban areas, nor to develop tailored solutions based on local data everywhere (3). This situation confines impactful climate action to a limited group of cities. There are entire regions with pressing mitigation and development issues that are left behind, in particular rapidly-urbanizing urban areas in the global South (4). The rise of big data and artificial intelligence offer new opportunities to model building energy demand for urban areas, and can even take into account geographical diversity (3). Such data could inform municipal policymakers on spatially stratified but city-wide strategies for climate change mitigation in buildings. However, large-scale spatially-explicit models that compute the minimum energy demand satisfying essential thermal comfort in buildings are still missing. Here, we propose a model that uses only open data for estimating the minimal energy use of individual buildings for heating and cooling at scale. The workflow is divided in two main blocks: (i) predicting at scale a 3D building stock, and (ii) estimating the energy use of buildings individually with a back-end model. In a first step, we use a machine learning model to learn and predict buildings heights from OpenStreetMap and ground truth LiDAR data. The latent energy use of a building can be approximated by its shape and its height (5). However, the height attribute is sparsely populated in OSM. Previous research (6) has demonstrated that building heights can be predicted from features describing buildings and their surroundings. We compare several machine learning architectures and input features spaces. In particular, we are developing a hybrid convolutional neural network based on OSM raster data and scalar features (7). Such architecture enables to take full advantage of both the spatial and the higher-level information available in OSM. We will test how well the model transfers to new cities, and how to improve generalization. In a second step, a back-end model computes the latent, minimal energy demand of each building for heating and cooling. The model accounts for simplified building characteristics, local climatological conditions and ancillary factors. Climatological conditions strongly influence building energy use. The latent energy demand is a simple metric that does not account for occupancy patterns or appliance use. However, this metric provides a lower bound for the energy necessary for thermal well-being, and it is simple enough so that it can be computed for any building. Preliminary results indicate that on average the energy demand in the studied areas can be reduced by a large amount while maintaining decent thermal comfort. However, strategies for energy demand reduction depend on building stock vintage, cultural standards, and the local climate. Our framework provides new insights from OpenStreetMap for more detailed and globally consistent analyses of mitigation strategies in cities. 1. N. D. Rao, P. Baer, “Decent Living” Emissions: A Conceptual Framework. Sustainability (2012) 2. O. Lucon et al., in Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel and J.C. Minx (eds.)] (2014) 3. F. Creutzig et al., Upscaling urban data science for global climate solutions. Global Sustainability (2019) 4. F. Creutzig et al., Urban infrastructure choices structure climate solutions. Nature Climate Change (2016) 5. P. Depecker, C. Menezo, J. Virgone, S. Lepers, Design of buildings shape and energetic consumption. Building and Environment (2001) 6. F. Biljecki, H. Ledoux, J. Stoter, Generating 3D city models without elevation data. Computers, Environment and Urban Systems (2017) 7. K. Tang, M. Paluri, L. Fei-Fei, R. Fergus, L. Bourdev, IEEE CPVR (2015)

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Providing more validation checks with MapRules and MapCSS Tag Checks in iD and JOSM to direct mappers to issues as they map. As well as using Overpass queries to retrieve features with data quality issues.

There is a need for validation in crowdsourced mapping to ensure that the quality of created data meets the community’s agreed upon standards and best practices. The OpenStreetMap community has created many Quality Control (QC) tools (Osmose, KeepRight, OSMLint, etc.) to identify existing errors within OpenStreetMap data, but there has not been as much emphasis on Quality Assurance (QA) tools to prevent issues from being created during the editing process. We are developing methods to introduce these data quality checks in both the iD and JOSM editors to educate mappers and provide immediate feedback while they are mapping. In order to introduce these new tools, we first need to recognize the methods a user typically uses to learn how to contribute to the map properly: - Peruse the many pages on OSM Wiki; - Reach out to community members on mailing lists, forums, or Slack groups; or, - Follow detailed instructions and receive feedback (sometimes untimely) on tasks completed in a focused project (via Tasking Manager or MapRoulette) This process is accepted and works for those who are resourceful and careful, but we wanted to reduce the barrier to entry for new mappers by creating MapRules. MapRules is an interface to create instructions which generate custom presets and validation rules that are then integrated into the existing validation frameworks in JOSM and iD. Contributors are directed on how to map features according to the generated rules and are provided instant feedback if their changeset does not meet the set specification. In addition to using MapRules to create specifications for collecting features, we are creating validation checks specifically for JOSM using MapCSS based on rules found in QC tools like Osmose, KeepRight, and OSMLint. This approach follows the paradigm of checking data before it is committed to the map. This approach is truly for all contributors and users of OSM. It clearly shows its worth to the mappers who are creating new edits, but it also aids validators to quickly identify problems where they may have had to visually and manually inspect each feature or rely on numerous QC tools outside of the editing environment. To further assist in validation and clean up, we have created a series of corresponding Overpass queries that download only the features with identified data issues. Within JOSM, these issues can potentially be resolved by applying automatic fixes on a feature or in bulk. By bringing more validation into the regular mapping workflow, we can help create a better map.

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OpenStreetMap enables people to use third-party apps that seem to be more suitable for privacy-conscientious users, but are we as users really private when using OSM-based apps?

In mobile applications users generally don’t have control over what gets transmitted over the network. This means that people even in the age of the GDPR are not aware what they expose about themselves when using Android or iOS apps. This talks goes through a few popular open and closed source OSM apps and shows what gets transmitted about you and to whom. Additionally, it shows whether removing ads through in-app purchase gives users of those apps more privacy. Furthermore it will be demonstrated which tracking services are most often used and which risks of de-anonymization could arise from this. Also a brief discussion whether and how software developers can make their software more privacy-friendly will take place. Are users aware which data gets sent to third parties and are they consenting? Do they have a choice? And is this tracking generally bad or can it improve user experience in return? Are there apps which are suitable for people who want to stay private? Is it possible to prevent tracking and how? This talk is going to compare several OSM apps in regards to their privacy levels, but also show how closed data, commercial competitors are performing.

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Travelers have high expectations of their route planners. We explore how preprocessing techniques applied to Linked Open Data derived from OSM (Routable Tiles) can provide a satisfying performance for client-side route planning.

Travelers have higher expectations than current route planning providers can fulfill, yet new solutions struggle to break through. Matching user experience from existing applications is already challenging without the large-scale infrastructure most of them have at their disposal; additionally integrating datasets such as the road network, public transportation schedules, or even real time air quality data is an even more laborious endeavour. W3C and OGC mention the usage of Linked Open Data as a best practice for publishing interoperable geospatial datasets. Instead of relying on proprietary data formats or monolithic CSV files, Linked Open Data uses the RDF data model as a framework for existing domain models. Every data element, and even the relations between them, receives a Uniform Resource Identifier (URI). Data publishers can reuse these identifiers to unambiguously refer to resources on the Web, thus making individual data sets more interoperable. The ultimate goal being automated integration, giving even clients the power to execute the queries. Client-side querying differs from traditional approaches but provides some advantages: (i) it takes the load off the service provider, (ii) the data can be cached for subsequent queries, and (iii) the user leaks less personal data. The OSM road network has recently been published as routable Linked Open Data, following a similar approach to vector tiles (http://pieter.pm/demo-paper-routable-tiles/). However, executing route planning queries on the client is still an unsolved problem. Long-distance queries require large amounts of data and downloading all the data takes a long time. It also makes caching less effective because caches have a fixed capacity, and writing to a full cache will evict other data. Moreover, even when all the data ingredients are there, processing them may still take a long time. State-of-the-art route planning algorithms achieve better query execution times by using auxiliary data that has been computed in a preprocessing phase. The biggest bottleneck in client-side querying is bandwidth; downloading more data to improve query times will ultimately make querying even slower. Client-side route planning requires a different approach to match the quality of service of existing services. We explore several ways of preprocessing the tiles to improve the user-perceived performance of query evaluation. As a first step, we compute how to efficiently traverse pedestrian areas. Only the boundary edges of these areas are defined in OSM which means that most routing engines route along these edges, yielding suboptimal paths. Secondly, we identify which nodes and ways are actually needed to cross an individual tile, filtering out the elements that are only used for local traffic. Queries only need the full tiles around the departure and arrival locations. Finally, a hierarchical network graph is computed using an adaptation of the contraction hierarchies algorithm. Each step yields a different view of the tile data and the results are published as Linked Open Data, in accordance with the W3C and OGC best practices. We integrated the results into a route planner for public transportation. The road network is used to route to and from public transport stops. We found that short-distance one-to-many queries such as getting to the closest nearby station that initially took around 400 ms to complete only take around 260 ms now, and the first results are presented after 140 ms. The difference becomes bigger over long distances; queries that used to take minutes to complete can now be answered in seconds. More importantly, downloading and querying the road network is no longer the main bottleneck. The majority of the querying time is currently spent on parsing the data files, which seems like a tooling issue that should resolve itself as the linked data ecosystem matures.

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This study adopts a statistical approach based on Exploratory Spatial Data Analysis to identify underlying contribution patterns of OpenStreetMap (OSM). Univariate and multivariate analyses on a number of variables computed from OSM history on a regular hexagonal grid in Milan (Italy) allow to detect a number of both local clusters and local outliers, which shed light on the complexity of OSM temporal evolution driven by active local contributors and communities, data imports and mapping parties.

Compared to traditional geospatial data sources, a major advantage of OpenStreetMap (OSM) is the availability of its full history. In literature, OSM history has been exploited for a number of purposes. The most frequent is intrinsic quality assessment, which - in contrast to extrinsic assessment, where OSM quality is evaluated through comparison against a reference dataset - estimates OSM quality by only looking at its temporal evolution. OSM history has been also explored to gain insights into the project's contribution patterns, e.g. history and profiling of contributors; origin, amount, nature and frequency of edits; spatio-temporal evolution of the whole OSM database - or parts thereof, such as road networks and buildings - in specific areas, or after specific events like natural disasters; and spatial analysis of contributor and contribution patterns. This work fits into the context of OSM intrinsic assessment by proposing a statistical approach based on Exploratory Spatial Data Analysis, and in particular spatial association, aimed at uncovering underlying history-based patterns of OSM data. More in detail, spatial association is investigated in both the univariate and multivariate contexts, i.e. in the cases - respectively - when one variable and multiple variables (together) are examined. The univariate analysis is performed using the Local Moran’s I indicator, which provides a robust classification method to detect statistically significant patterns (compared to the hypothesis of randomness) and define the spatial association type at each location in the dataset. The association type reflects the local characteristics of the variable at each location and its surroundings. Hence it allows detecting clusters, i.e. local patterns of similar (either high or low) values, as well as outliers, i.e. local patterns of dissimilar values (either low values surrounded by high values or viceversa). Instead, the multivariate Geary’s c indicator is employed to detect local association patterns resulting from the joint spatial interaction of two or more variables. A multivariate pattern classification comparable to the one of the univariate case is achieved through a novel classification method developed by the authors. This consists of a comparison of local and global centrality measures (means and medians) for the computed distribution of the multivariate Geary’s c, to produce classification maps of clusters and outliers. The analysis is performed on Milan Province (Northern Italy), counting a population of more than 3 million inhabitants on a surface of about 1.500 km². This area is sampled using a regular hexagonal grid with side of the hexagon equal to 1000 m, producing a total of 684 cells. The analysis is focused on the history of OSM nodes only, with the following hypotheses: only nodes with at least one tag are considered; a new version of a node is counted only when there is a change in tags (not in geometry); only the nodes which currently exist in the OSM database are considered. With this in mind, for each grid cell a number of history-based variables are computed: total number of different contributors who have edited OSM nodes; average number of different contributors who have edited each OSM node; average date of creation of the OSM nodes; average date of last edit of the OSM nodes; average number of versions of the OSM nodes; average frequency of update of the OSM nodes. These values are derived from the processing of the OSM Full History Planet file (downloaded in May 2019) and its conversion into a SpatiaLite database after an intersection with the study area, followed by the computation of the variables for each grid cell. The univariate analysis, performed using the QGIS Hotspot Analysis plugin developed by the authors, highlights different spatial associations for the different variables. While some of them (such as total and average number of contributors and average version) clearly show clusters of high values in correspondence of the most urbanized areas and clusters of low values in the non-urban peripheral areas, spatial association patterns are more heterogeneous for other variables such as the average update frequency. Multivariate analyses are then performed to detect the spatial patterns derived from the joint interaction between two and more of the variables considered. Despite each variable has its own spatial pattern when taken alone, their combination (especially when adding more and more variables) highlights not only high and low-value clusters in urban and non-urban areas, but also other interesting clusters and outliers. These unveil peculiar contribution patterns resulting from active local contributors, data imports and mapping parties, and highlight areas where OSM development might need some improvement. Despite only preliminary, the methodology - which, to the authors’ knowledge, has been never adopted before in OSM-related research - looks extremely promising to process the complexity of OSM history and transform it into understandable, statistical-based indicators which can shed more light on the intricate phenomenon of OSM local development.

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The OSM community is global and diverse. Building on last year's Open Heroines conversation, we will co-create a space for OSM to talk about how to improve diversity and inclusion in our amazing project. All welcome.

How can OSM be more diverse and inclusive? What can we do to improve this across all the various OSM spaces, including OSM governance? Diversity is the range of human differences, including but not limited to race, ethnicity, gender, gender identity, sexual orientation, age, social class, physical ability or attributes, religious or ethical values system, national origin, and political beliefs. Join us to share your lessons and ideas on how we might grow and support a Diversity and Inclusive approach in OSM. There are Diversity and Inclusion groups across other 'open' communities. OSM has a diversity talk mailing list. This will be the place where the community can continue to connect after SOTM. The goal of the session is to give space for the OSM community to talk about their needs and to plan a way forward to supporting OSM on this journey. We will ask participants to co-create plans and identify how we might incorporate it into small and big activities within the global network. The format will be co-created with some potential outcomes. The goal is to be a conversation with interactive, participatory methods and some small group work. Examples of formats: https://facilitation.aspirationtech.org/index.php?title=Agenda:Sessions See more about this topic: https://blog.mozilla.org/internetcitizen/2019/03/04/open-source-inclusion/ https://opensourcediversity.org/ https://github.com/mozilla/diversity

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Protecting OpenStreetMap is a continuous process performed by Mapbox to secure maps from displaying erroneous edits. Any edits that raise suspicion are flagged in OSMCha, an open service that allows to check low-quality changes that are made by the members of the OSM project in a shared database. This not only helps to report our findings to the community but examine them in aggregate and draw conclusions to improve our data quality processes.

It is important that the reviewer has the necessary information about the changeset and understand the diff of modifications in the OSM data. For this purpose, OSMCha offers an interface similar to OpenStreetMap but with additional tools such as changeset-map to visualise the edits, information about the mapper, other information related to the changeset and OSM user history that can help the reviewer identify problematic edits on OSM. During this workshop the following aspects of work with OSMCha will be highlighted: - the basic principles of work with the interface, - analysing data on the map view - flagging changesets as `bad` or `good` with varying levels of severity - getting information about changeset, data and contributor - the possibility of filtering out the data in a granular approach that helps to limit the amount of information about changes in OSM, in terms of - contributors, where you can pick up the data for a particular mapper(s) or mapping teams - the date range, you can limit a number of changesets by the dates of submission to OSM and by the dates when they were reviewd in OSMCha - the reasons for suspicion, OSMCha has a range of internal functions that assign different reason for suspicion to changeset that you can use for reviews - area, where you can set up area of interest for edits inspection The main goal of the workshop is to engage the community to evaluate the existing data using OSMCha and improve its quality.

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Indexing OSM data in your PostGIS database for fast spatial queires is not as straight foreward as one migth hope. And with each release of PostgreSQL / PostGIS there are more options to try out. This talk will explain different spatial indexing concepts and best practices in PostGIS and present some benchmarking results.

When working with OpenStreetMap data in a database you learn pretty soon that you need to index the columns (and rows) which you use for filtering in order to have fast running queries. PostgreSQL offers a variety of these [table access methods](https://youtu.be/W6B8-srOsrs) but when it comes to its spatial extension PostGIS, developers could only use one for the geometry fields for years: The GiST index - an implementation of the [R-tree](https://en.wikipedia.org/wiki/R-tree) search tree concept. But during the last releases new methods were made available, namely BRIN and sp-GiST. Not many resources are yet available to figure out which index strategy to apply for which data processing or analytical workflows. Therefore, I created a simple [benchmark](https://github.com/FxKu/postgis_indexing) to find it out. So far, only for artifical data, but for this years StoM it is planned to extend the experiments to OSM datasets. This talk will explain the different characteristics of each spatial index and present some performance comparisons in terms of query speed, overhead on writes, index building time and index size. It will also cover general indexing best practices such as subdividing geometries, [partial indexing](https://wiki.openstreetmap.org/wiki/User:Species/PostGIS_Tuning#Indices) and introduce new concepts such as [covering indexes](https://info.crunchydata.com/blog/why-covering-indexes-are-incredibly-helpful).

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JOSM is almost as old as OSM but few people really know what it takes to maintain your preferred editor. We'll present the development model of JOSM and who's part of its active community: developers, translators, testers, plugin authors, end users, sponsors, etc. We'll talk about the project difficulties, the major achievements made in the past years, what work is currently in progress and what will happen in the near future!

JOSM is almost as old as OSM but few people really know what it takes to maintain your preferred editor. We'll present the development model of JOSM and who's part of its active community: developers, translators, testers, plugin authors, end users, sponsors, etc. We'll talk about the project difficulties, the major achievements made in the past years, what work is currently in progress and what will happen in the near future!

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We are working to map all the solar panels (photovoltaic, "PV") in the world. Why? The data can be used directly to reduce carbon emissions from power generation. We will share our experiences of surveying, aerial mapping and machine vision to find all the hundreds of thousands of solar panels in our countries.

Together with a small group called OpenClimateFix, we are working to map all the solar panels (photovoltaic, "PV") in the world. Why? Because if we combine this with short-term forecasting of cloud and sunshine, we can directly predict the solar power electricity generation ahead of time. This means we don't need to burn as much coal or gas as backup. So, it can be valuable to know the exact location and characteristics of each solar PV installation - large solar farms, and small domestic installations. In this talk we'll discuss how this all fits together. We'll talk about solar power tagging/mapping in OSM, for both large and small, to make it easy for people to map but also useful for the power network analysis. We'll also share our explorations of crowdsourcing and automation machine vision, and the use of other data sources such as government open data to guide the mapping process. We have already mapped a significant portion of the UK's solar capacity, and trialled some crowdsourcing and machine vision tools. We will show visualisations and analyses of the work that's been done so far, and consider how to scale this worldwide.

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More than 17 million edits globally have been made by paid contributors in the last five years. We look at the long history of corporate involvement in OSM and then dig into the data to quantify the impact this latest evolution of corporate involvement is having on the map and explore the interactions between paid and volunteer mappers.

OpenStreetMap (OSM) is both a map and the active community of over a million mappers that create and maintain it. Participation in OSM has largely been studied in terms of motivation and the resulting data quality. Today, the community is comprised by many different interest groups including craft/hobby mappers, humanitarian mappers, professional mappers, and more. The last few years have seen a dramatic growth in a specific group of mappers: corporate editors. These are mappers hired by corporations and edit the map as part of their employment. In November 2018, the OSM Foundation published the _organized editing guidelines_ that outline a number of steps all groups engaged in organized editing activities (including corporate data teams), should take to promote transparency, openness, and engagement with other mappers—especially local community. This work identifies ten corporations that are complying with these guidelines and explores their mapping activities. We found these corporations have cumulatively edited over 17M objects globally in the last five years, of which 9M were edited in 2018 [1]. First, we traced the history of corporate involvement in OSM to show that while this growing phenomena of corporate editing is new, it represents just the latest stage in a long history of corporations both contributing to and benefitting from OpenStreetMap. Next, we used historical quarterly-snapshot OSM-QA-Tiles to quantify where the ten corporations are active on the map and what types of edits they are performing. We find these edits are global in geographical scope, yet vary per corporation in location and edit type: Corporations heavily impact road networks, yet non-corporate mappers maintain the majority of all edits by mapping more buildings and points-of-interest [1]. To date, this research has quantified and contextualized the growing phenomena of corporate editing in OSM and identified the need for more in-depth analysis to more descriptively explain the impact to the map and volunteer mappers in these regions where corporate-editors are active. To further explore these impacts, we need to dig deeper into the editing record to describe the evolution of the map. For this, we are building upon open-source OSM data-processing tools to construct new vector tiles with with full OSM editing histories [4]. These new historical analysis tiles allow us to efficiently explore the evolution of the map in these regions. This allows us to better contextualize and visualize the interactions between corporate editors and volunteer mappers at scale. Previous research has shown that the road network typically gets mapped first and the map builds up from there [2]. To this extent, we will explore the notion of _map seeding_ whereby paid editors create the first version of the road network, seeding the map for others to maintain and grow. Supporting such an idea is the concept of _densification_ of the map, where some mappers prefer to edit where there is existing—though incomplete or sparse—map data, instead of a beginning with a blank section of map [3]. The concept of such editing patterns highlights the nuances in effectively measuring the impact of paid editing on the map. In other words, this question is more complicated than “are corporate editors taking over?” The first part of the research presented here was recently published [1]. The deeper exploration of the data to identify and explain the impact to the map and local communities is current, ongoing research. At State of the Map, I will briefly summarize the completed work to better set the context, and then present the results of our ongoing investigation on the impact to the map and community. 1. Anderson, J., Sarkar, D., & Palen, L. (2019). Corporate Editors in the Evolving Landscape of OpenStreetMap. ISPRS Int. J. Geo-Inf. 8, 232. 2. Ciepłuch, B., Mooney, P., & Winstanley, A. C. (2011). Building Generic Quality Indicators for OpenStreetMap. 19th Annual GIS Research UK (GISRUK), 5. Retrieved from http://eprints.maynoothuniversity.ie/2483/ 3. Corcoran, P., Mooney, P., & Bertolotto, M. (2013). Analysing the growth of OpenStreetMap networks. Spatial Statistics, 3, 21–32. https://doi.org/10.1016/j.spasta.2013.01.002 4. OSM-Wayback Utility available at https://github.com/osmlab/osm-wayback

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This presentation describes the nature and life-cycle of carto-vandalism through a data-driven analysis of harmful edits originated from Pokémon Go players. It also assesses how the OSM community reacts to vandalism.

The utilization of OpenStreetMap (OSM) data by mainstream tech companies has been on the rise in recent years. Two prominent examples are Snapchat and Pokémon Go that became OSM data consumers in 2017. Snapchat [reports](https://bit.ly/2YAzv70) 190 million daily active users in 2019. Pokémon GO was used by 28.5 million users daily during its peak popularity in 2016 and it still managed to engage more than [10 million](https://bit.ly/2VJS3EO) users monthly in 2018. The large user base of these application puts OSM in an unprecedented spotlight which can be considered a huge success for the project. On the other hand, increased attention comes with side effects. Acts of [vandalism](https://bit.ly/2HBLE4R) manifested in the data no longer stay within the OSM community but will be visible to a worldwide audience. This increased visibility of errors caused by malicious actions (e.g. fake place names, fictive data) can potentially undermine the reputation of the OSM project. In August 2018, a case of [anti-semitic vandalism](https://bit.ly/2HHPRno) surfaced on Snapchat's online maps and also made it to various mainstream media outlets, such as the [BBC](https://bbc.in/2JLO4QY), [Time](https://bit.ly/2VJwpkd) or [The New York Times](https://nyti.ms/2onZJJS). Another type of vandalism can be observed in connection with Pokémon Go, where users modify the underlying OSM data by adding [fictional map features](https://bit.ly/2EjFSnD) (e.g. parks, footpaths and lakes) to gain benefits in the game. OSM’s vulnerability to vandalism is often considered one of its drawbacks directly related to data quality. Despite this and other negative effects on the OSM project, carto-vandalism [1] has only been addressed sporadically in the literature. One study identified motivations behind such actions [2], while some other studies characterized different types vandalism based on investigations of community forums and mailing lists [1] and documented cases of vandalism [3]. According to Linus’s law, the collaborative nature of OSM ensures that vandalism will be discovered and corrected [4]. However, it is unreasonable to expect that all harmful contributions will be found by community [5], therefore, automatic detection of vandalism with rule-based methods is of interest [3, 6]. The OSM community also developed a set of tools to battle vandalism. Using Pokémon Go as an example, this study focuses on the nature and life-cycle of harmful edits with an emphasis on the OSM community's response. Based on OSM changeset comments and discussions, the study first identifies Pokémon related vandalism together with changesets that fixed them or reverted them. This duality allows to study not only the act of vandalism itself, but also the community's response. By analyzing changesets committed by Pokémon Go players, the presentation describes in detail what kind of fictive information these players tend add. A better understanding of this will allow to develop more targeted rules for vandalism detection systems. However, it is important to note that not all Pokémon Go players vandalize OSM and some of them are probably valuable members of the OSM community. It is less obvious, though, if those who started their OSM careers as vandals can be converted to real contributors. As OSM relies heavily on its community, this study seeks empirical evidence of vandals converted to OSM contributors. This would help utilizing OSM's increased visibility to engage and retain more contributors. Vandalism directly affects data quality, therefore we provide a first description of the life-cycle of carto-vandalism analyzing a large pool of events and considering both spatial and temporal constraints. Our initial data analysis shows more than 1,000 revert changesets in response to Pokémon Go vandalism. Since OSMappers often revert more than one changeset at once (e.g. https://bit.ly/2YC7U5f), this number corresponds to more vandalism cases in reality. This provides sufficient amounts of data for our investigation. [1] Ballatore, A. (2014). Defacing the map: Cartographic vandalism in the digital commons. The Cartographic Journal, 51(3), 214-224. [2] Coleman, D., Georgiadou, Y., & Labonte, J. (2009). Volunteered geographic information: The nature and motivation of produsers. International Journal of Spatial Data Infrastructures Research, 4(1), 332-358. [3] Neis, P., Goetz, M., & Zipf, A. (2012). Towards automatic vandalism detection in OpenStreetMap. ISPRS International Journal of Geo-Information, 1(3), 315-332. [4] Haklay, M. (2010). How good is volunteered geographical information? A comparative study of OpenStreetMap and Ordnance Survey datasets. Environment and planning B: Planning and design, 37(4), 682-703. [5] Goodchild, M. F., & Li, L. (2012). Assuring the quality of volunteered geographic information. Spatial statistics, 1, 110-120. [6] Truong, Q. T., Touya, G., & De Runz, C. (2018). Towards Vandalism Detection in OpenStreetMap Through a Data Driven Approach. GIScience 2018. Melbourne, Australia. 61:1–61:7

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Here is the **Nomad Maps documentary**: 5 months, 5200 kilometres across the Andes, 100 000 metres of positive altitude difference, all by bike, to meet the local contributors and projects of the OSM mapping community of Colombia, Ecuador and Peru. An alternative way to see the uses of the OpenStreetMap !

Nomad Maps is a project carried out in partnership with the French NGO *CartONG*, created by [Alban Vivert](https://www.linkedin.com/in/alban-vivert-664331168/.), a French geographer and cartographer, passionate about mapping, travel and cycling. Financed by a crowdfunding and a grant from the *Fondation de France*, *Nomad Maps across the Andes* had several objectives during these 5 and a half months of travel: 1. Mapping on OpenStreetMap and Mapillary: more than 10,000 nodes mapped on OSM and more than 110,000 mapillary pictures added. 2. Meet local OSM contributors: 16 contributors met and participated in the SOTM Latam 2018 in Buenos Aires. 3. Participate in micro collaborative mapping projects with local associations. 4. Develop CartONG's Latin American partnership. 5. Develop a community of "NomadMappers": a community of travellers who want to make their trip useful by mapping the countries visited on OpenStreetMap. Nomad Maps documentary deals in 28 minutes with the second goal of the project, more exactly the meeting with local OSM contributors from Colombia, Ecuador and Peru. This documentary has different targets. For OSM experts, the aim is to give visibility to Latin American projects. For the non-specialists, the aim is to show the impact and relevance of OSM mapping in the humanitarian and development community in Latin America. To see more about Nomad Maps: Nomad Maps website: http:/www.nomadmaps.net Map of the route: http://nomadmaps.net/andes Blog: https://nomadmapsblog.wordpress.com/ Facebook: https://www.facebook.com/NomadMapsProject/ Instagram: https://www.instagram.com/nomadmapsproject Twitter: https://twitter.com/MapsNomad

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The talk describes the use of OSM as part of an asset management process using a crowd of National Trust staff, volunteers and the public to maintain an network inventory of an estimated 20,000 km of paths (both Public Rights of Way and permissive paths). The process pro-actively notifies local staff of changes to enable on-ground validation.  The process required the definition, and consistent application of, a UK standard for path tagging.

The National Trust (NT) is a charity set up to look after special places in England, Wales and Northern Ireland. Now the largest land owner in the UK, NT cares for more than 250,000 hectares of land including 775 miles of coastline, 100,000 hectares of statutory wildlife sites, 28,000 buildings and structures, 300 historic mansions and gardens. The NT operates a membership business model which provides members with complimentary access to our pay-for-entry visitor attractions, but also seeks to provide extensive free access to the countryside. The pay-for-entry visitor attractions welcomed 2.5 million visitors in 2018, and an estimated 300 million visits to countryside sites. Recent internal analysis estimates that NT look after a network of 20,000km of footpaths which comprises both legally designated ‘Public Rights of Way’ (25%) and permissive paths (75%). Permissive paths are those where access to members and/or the public is provided, and the condition of the path is monitored. Currently there is no single digital inventory of Public Rights of Way or permissive paths in the UK. The Paths Project is capturing a digital inventory of paths on National Trust in order to: • Demonstrate how NT are fulfilling our core purpose of providing access to special places. • Improve asset management including maintenance and enhancement of the path network, • Provide a digital base for trail curation and enhanced visitor experiences. A review of options concluded that the OpenStreetMap would be the best system for management of the National Trust Paths Inventory. This enables the capture of a digital path data to a defined standard to be carried out by the Trust’s extensive local staff, volunteer network and also any citizens with an interest in paths.  The data is then available royalty-free to anybody for the development of products to encourage appropriate access to our special places. This approach is not without challenges. The constancy of path tagging in OSM is not currently suitable for describing the access to the path network. In order to ensure the paths data met the NT needs we worked with OSM UK, and other partners to apply consistent data standards for tagging paths in OSM. This standard is specific to the UK context and enables to differentiation between legal rights of way and permissive paths. In working with local staff to review the current representation a number of challenges were identified relating to the official register of paths and the reality on the ground. This is an evolving area and as the project progresses it is expected that other challenges will emerge. The project set-up an automated process for notifying local staff to changes in their area of knowledge so that changes could can be validated against the reality on the ground.  This work provided an interesting technical and organisational challenge and is a somewhat novel approach for large asset owning organisations seeking to utilise OSM and the power of the crowd to manage asset data. The initial focus of the project is on the existence of paths and the legal basis for access. Once this is captured additional attributes relating to accessibility, condition will be considered. Whilst this project is still to be completed, other potential asset types that could be managed in OSM in this way have been identified. The true value of a UK wide paths inventory will only be realised by having a comprehensive, authoritative and trusted dataset. This cannot be achieved by NT alone. When the project is suitable mature NT will make available any parts of the process to other organisations to encourage adopt a similar approach.

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The PostgreSQL database with the PostGIS extension is an important instrument in the toolbox of anybody working with OSM data. This talks explains the basics of working with the SQL database and how it handles geographic data. We'll look at getting OSM data in and out of such a database and what we can do with the data once it is in there.

The PostgreSQL database with the PostGIS extension has always played an important role in the OpenStreetMap project. The main OSM database is a PostgreSQL database, tile rendering is most often done from a PostgreSQL database, data analysis and data transformations using current or historical OSM data can be done with PostgreSQL. But understanding what the database can and can not do isn't always easy. And there are a ton of tools, from osm2pgsql to Imposm to Osmosis to Osmium and beyond that "do things" with OSM and PostgreSQL databases. It can be daunting to understand what they all do and where their place in the larger OSM ecosystem is. This talk is an introduction into some of the concepts of SQL and geographical data in an SQL database as well as a whirlwind tour about uses of PostgreSQL in the OSM context. We'll talk about use cases from rendering, to data analysis to routing. We'll talk about the data models that enable those use cases and the software that implements them. And we'll also talk about where the limits are and what things can better be done outside the database. The audience will walk a way with an idea of how things fit together and how to approach their own projects using PostgreSQL.

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In this talk, large scale data production events in OSM are identified, characterized, and their spatio-temporal patterns and impacts are analyzed. The results show that remote mapping events produce more data today than bulk imports, yet that the former type has a more lasting impact on representation, hence pointing towards possible steps for maximizing the positive influences of events of different types.

Volunteered geographical information often visions data as a product of individual actions. In OpenStreetMap (OSM) however, contributions are frequently made as part of large-scale data production events. These events, which can take multiple forms (e.g. organized activities of local chapters, mobilization of global communities, and imports of externally collected datasets), do contribute much to the OSM project. Nevertheless, they also hold the potential to significantly affect local representations by changing the development course of data and community, thus biasing representation. Hence, it is important to identify and understand such events, as well as their impacts upon the data. This talk sets out to contribute to the study of these issues by identifying large-scale data production events in OSM, classifying them, and analyzing their spatio-temporal patterns and impacts. For this, we use the OSM History Database (OSHDB) tool to extract the cumulative number of contribution operations (i.e. the operations made as part of each contribution) by month for different areas. Assuming that in the absence of events the cumulative distribution of monthly operations over time would follow an S-shaped form (since data grows constantly, and even more so when the community grows, until it reaches some form of saturation), we fit a logistic curve to each of these time series. Events are identified as months where observed values are significantly higher than the ones predicted by the fitted curve. Thus, events are defined not only in terms of their absolute size but also according to their relative weight in the development of the data. In the subsequent step, events are clustered into types according to different measures, e.g. the maximal number of contributions made by one user and the share of creations, deletions, tag changes, and geometry changes in all contributions, representing their nature in terms of centralization and contribution themes. The results show that a significant share of all OSM contributions is made as part of an event, with some data regions almost entirely dominated by these. Furthermore, it does not seem that the number of event contributions is decreasing over time. Looking deeper into the nature of events, we identify two different event types based on the contribution of individuals – local events and remote mapping events – and several bulk import event types, diverging mostly in the share of creations in the events’ contributions. Computing the number of events over time shows that while data creation imports were the most frequent type of events early on, over the last years remote mapping events are contributing the most data. Locally based events also show a significant increase in data production. However, these types of events are not distributed evenly across the globe, with import events frequent mostly in countries with developed economies and remote mapping events being more common in the least developed regions of the world. Interestingly, the negative (and expected) correlation between the time of the event and its impact on the data exists only for import events and not for remote mapping events. Hence, mapping and analyzing large-scale events allows relating the nature representation to socio-economic effects. This talk further breaks down the spatio-temporal patterns of events, investigating whether the temporal patterns for different regions follow the global ones or are there clusters of temporal change as well. Furthermore, we study the nature of events’ impacts, presenting how the values of measures such as the stability of events’ contributions and change in the number of active mappers vary by event type and area. These results, beyond promoting a deeper understanding of events and representation in OSM, allow assessing the implications for the project of current and expected trends in OSM data production, hence facilitating the formulation of global and local steps aimed at maximizing events’ positive impacts and controlling their adverse influences.

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The project created from the cooperation between GeoLab (Universidad de Antioquia), and SiCLas (group of cyclists), both present in Aburrá Valley (Colombia), proposes a collaborative mapping by bicycle users as an urban transport mode. The data generation from existing cycling infrastructure will allow an improvement of OSM database, and an optimization in route calculation. In addition, the incorporation of surveys will allow the generation of thematic maps, such as the association of gender with mobility.

The cities of the Aburrá Valley, with Medellín as a core city, present a network of bike paths, and the theme of sustainable and non-motorized mobility is gaining increasing importance in public policies in the region. Meanwhile, everyday cyclists, who use the bicycle on a daily basis as a mode of transport, know that this cycling infrastructure has lacks in several aspects, such as a fragmented presence in the territory, non-direct routes, among others. In addition, the cycling infrastructure in OpenStreetMap is not completely mapped or has erroneous information, this represents a problem for cyclists who wish to transport themselves through the city and identify the most appropriate route to their destination. It does not have accurate and relevant information for users such as the location of bicycle parking, bike-share stations and multimodal stations, among others. These reasons represent a disincentive for more people to choose the bicycle as a mode of transport. For these reasons, this project aims to map the existing formal cycling infrastructure, urban transport routes used by cyclists, to optimize route calculation, and aims to create a platform that allows the visualization of safety data, infrastructure, environmental problems (air quality) and gender mobility, among others, in which the information of shared eventualities can be collected and uploaded by the users. From the cooperation created between the GeoLab research group of the University of Antioquia – YouthMappers chapter, and the SiClas group of cyclists, both entities present in the Aburrá Valley, a considerable number of cycling activists will be mapping the cities of the valley with the OSM platform and tools open. The discussion of the existing tags in OSM for the mapping of cycling infrastructures is a topic of interest in the project, and can contribute to a discussion of the creation of new tags in OSM. The status of current information will be verified and corrected and the missing routes mapped. The tools to generate the thematic maps will be KoBo Collect, Mappillary, among others.

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Most of the existing attribution guidance for OSM derived works dates back to 2012 and was written before or around the change of the OSM licence to the ODbL. While there have been relevant discussions and rulings by the OSMF LWG (Licence Working Group) since then, there is no easy way to find it in one document. The LWG has undertaken to review the existing guidance and update it where necessary and is now asking for community input.

Most of the existing attribution guidance for OSM derived works dates back to 2012 and was written before or around the change of the OSM licence to the ODbL. While there have been relevant discussions and rulings by the OSMF LWG (Licence Working Group) since then, there is no easy way to find it in one document. The LWG has undertaken to review the existing guidance and update it where necessary and is now asking for community input.

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There are 250 million refugees and IDPs in informal settlements that are routinely excluded from population and settlement datasets as well as Sustainable Development Goals (SDGs) assessments. Here, we share results from ongoing research to map and assess SDG indicators at global informal settlements using OSM data and satellite imagery. We present a new OSM-driven schema for monitoring SDG progress that counters the exclusion of informal settlements from other assessments.

In 2015, the United Nations introduced 17 Sustainable Development Goals (SDGs) and 169 associated targets to be met as part of its 2030 Agenda. One hundred and ninety three countries declared their commitment to “leave no one behind” in the shared pursuit of SDGs, yet 250 million people around the globe are estimated to be missing from SDGs progress assessments. This presentation focuses on one category of the “Missing Millions”: refugees and internally displaced people (IDPs) living in informal settlements in over 60 countries. In part due to the lack of reliable information on the locations of informal settlements, refugee and IDP populations are systematically excluded from national censuses, representative surveys, global settlement and population data sets, and settlement-level SDG progress assessments. If implementation and monitoring of SDG goals are to be truly inclusive of these highly vulnerable populations, a globally systematic, accurate, and open database of informal settlement locations is required. In order to locate and characterize informal settlements across the globe, we have used multi-sensor satellite (i.e. Landsat, Sentinel-2, and Planet Dove) imagery, machine learning pattern recognition, and crowd-sourced data collection through visual interpretation of high resolution satellite imagery. With informal settlements identified and using Uganda as a relatively data-rich case study, we developed a spatially discrete and temporally informed settlement-level schema for monitoring SDG initiatives and evaluating progress since 2015. To do so, we examined the distribution of OSM tags relevant for SDG goal indicators, built a database of unique OSM flags for specific SDG indicators, identified relevant OSM features within and nearby informal settlements in Uganda, measured when each feature was first added to the OSM database, and assessed the variability in SDGs progress across a global sample of informal settlements. The presence of SDG-relevant OSM features at some informal settlements may reflect past humanitarian or development mapping, while other informal settlements with less attention in OSM may lack any SDG-relevant OSM features. Since we also sought to assess SDG indicators purely through satellite image analysis, we measured the agreement and complementarity of various remotely sensed measurements of SDG indicators and OSM features. Amenities or public services represented in OSM data such as the designation of a building as a school or health center, and sites of portable drinking water have been essential to formalizing the relationship between satellite-derived estimates of SDG proxy indicators and SDG-relevant features in OSM since these features are not readily identified with satellite imagery and thus are complementary to satellite image-derived indicators. Finally, with an eye on scaling up our assessment to the global-level, we evaluated the relevance of the Uganda-based SDG schema for other geographic regions and specific informal settlements. The results of this research highlight the value of fusing place-specific OSM data with spatially expansive satellite imagery for assessing progress toward specific SDGs. Further, the analytical framework developed for this project may inform future OSM volunteer campaigns to map and evaluate settlement attributes of vulnerable populations or help guide development practitioners and programming. Many challenges associated with the considerable variability in OSM tags within and between informal settlements remain for developing a settlement-level schema for assessing SDG progress using OSM data. Further, the absence of SDG-relevant OSM features across so many informal settlements highlights the need for a rigorous assessment of potential bias in settlement-level OSM data availability. Future work thus includes a cross-validation between remote sensing and OSM-derived SDG indicators at informal settlements as well as field-based assessment of the settlement schema. Results from this effort will inform how on-the-ground contributors and global end users of OSM data can adopt the proposed settlement SDG schema to better align data collection for SDGs assessment.

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An analysis of Notes, based on local experience of managing notes.

Over 1 735 319 notes have been added to OSM, currently 416 224 notes are still open. Even looking at a small area such as Edinburgh, the city I live in, we have around 10 new notes added every day. It's interesting to note the motivation of people adding notes, many are added by regular experienced mappers for changes that need to happen in the future, some are added by local businesses or anonymous users altering us to problems on the map and some by non technical users for changes that are required. These note encapsulate a few different categories: Things that can be fixed quickly and the note closed straightaway. We then have short to medium term notes, such as shops closing or road closures that may be for issues for between days and months. Then finally then some of the very long term items, such as the construction of the Forth Crossing, New Hospitals or Housing Estates that may be tracking developments for years. But with so many notes remaining open, issues can very easily be missed. I will briefly review some of the tools available for tracking notes, and talk about how we deal with notes including some of our local tests with using github issues to track some of the longer term issues. Finally as well as hopefully look at some ways of managing notes. Hopefully with some prototypes for ways that notes might be improved either directly on the OSM site or via an external site.

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OpenStreetMap's processes are carefully designed to minimize the privacy footprint of the mappers. Nonetheless, the principle that any edit shall be attributable means that some data is still recorded. An overview is given which data is recorded at all and which of it gets available to whom.

OpenStreetMap surveys data about places, not about persons. Thus privacy sensitive data in the geodata is very rare and always unwanted. Likewise, the principle of distributing the data allows data consumers of the geodata to stay as anonymous as they want. Mappers can contribute with pseudonyms, although not completely anonymous. However, for both the social features of OpenStreetMap and the purpose of attribution for the edited data it is necessary to collect some privacy related data. A useful starting point to see your own data footprint is the tool HDYC. All the edits you make are grouped into changesets, and the editing software will urge you to add a comment per changeset. As both the edits and the changesets carry dates it is possible to track your activity times. The nature of some edits may suggest local knowledge thus that you actually have been at the mapped places. The kind of edited objects and the mapping standards can be checked by every other contributor as well. Most often, this is simply a reason to be proud of the achievements, but you should know and check before it might get a matter of concern. Using separated accounts for different mapping tasks is often an appropriate solution and well-accepted in the community. OpenStreetMap offers additional ways to interact: you can leave comments on changesets and notes on the map, you can discuss on mailing lists or the forum, or you can answer questions on help. You can write documentation on the wiki. All of these channels have their own rules with regard to how long the data is stored, how it is searchable, and whether you can hide or delete your contributions. The talk presents the data traces of each channel and discusses strategies to get the desired degree of privacy.

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Using JOSM Plugin to mapping road in advanced way and improving data quality in OpenStreetMap.

This workshop will examine about a Turn Restricition Plugin in JOSM. Nowadays, popularity of OSM has been spread all over the world. Moreover, based on development of OSM itself, many parties has been used it in any sectors including routing services. However, road mapping is not as popular as POI Mapping eventhough road data is also important as polygon or buildings in OpenStreetMap. So far, OSM mappers mostly focus on mapping public facilities and POI to complete their area in OpenStreetMap. Thus, this workshop will be focused on road mapping in JOSM using turn restriction where the participants can learn more about mapping road in advanced way. Moreover, this workshop goal is to improve more knowledge around OSM mappers community that can lead to improving Data Quality in OSM especially roads. In this session, I will share my experiences as OSM mapper how to use this plugin. A lot of interesting experiences that I had such as how implemented this plugin in some of my mapping project activity and what challenges that I had and how to manage with it. Finally, I will share to all of the participants about the result in some area. Comparison between before and after OSM data in each city and the benefit that we could get from the data to OSM community in Indonesia.

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OSM Teams is a software framework for building team-based applications on top of OpenStreetMap. We will present how the software is built, why we think it's a good tool for communities, and how you can integrate your application with the framework.

OpenStreetMap is first and foremost a community platform. A lot of OSM mapping emerges from grassroots collaborations, from local neighborhood communities to large-scale mapping initiatives. These collectives need tools to communicate, collaborate and sustain their combined motivation around mapping, and to that end we present OSM Teams. OSM Teams is a software framework for building team-based applications on top of OpenStreetMap. Development Seed built Teams internally to coordinate mapping projects, and to share tasks and statistics across mapping applications built on top of OSM. By building an authentication and authorization framework on top of the OSM login, we enable a second identity layer for teams that can be shared throughout apps. If adopted by the wider community, OSM Teams would bring structure to organizational editing. Teams would also support individual mappers by empowering them to discover new communities and causes, and to better understand who else is editing the map. With this presentation, I would like to start a technical and community focused conversation about running organizations, teams, and communities using this framework. Specifically the questions I would like to bring to the community are: how does this fit with existing community patterns? How do we reduce complexity for new mappers? What new application possibilities does this open up?

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The OSM data model has facilitated rapid growth of community-created geodata which third parties can build on. But as more accuracy is needed in routing, cartography, and other uses, is this data model good enough? We are trying to represent spaces as flows, which result in fundamental compromises and inaccuracies. This talk will discuss real-world cases where this compromise is increasingly problematic.

OpenStreetMap was designed to enable ordinary people to create open geodata that anyone can use and maintain easily. Traditional GIS concepts such as layers are dispensed with in order to make editing simple and accessible. In the same way that the web would never have taken off if HTML were not so accessible and tolerant of mistakes, this simplicity in OSM has meant a low barrier to involvement. However, as OSM is becoming more widely used in the mainstream, the need for accuracy and quality is becoming more and more important. Cyclists need detailed turn data to enable high-quality routing that takes full account of safety. Satnav companies, need lane data, which is difficult to represent accurately. Pedestrian routing is barely in its infancy and high quality routing for people walking or using wheelchairs is hard to achieve. At its root, OSM tries to represent spaces as flows (lines). This results in fundamental compromises and inaccuracies. What is good for routing is not always good for cartography, and vice-versa. For instance, a street containing cycleways with pavements either side is usually represented as a single line with attributes. However, it is extremely challenging to represent properly all the parts of the street, and in general people simply don't bother: a single line with large numbers of attributes is unwieldy to edit (even when hidden by editor GUIs), and just as challenging for a router to interpret. Continual changes in the width of a street cannot be easily represented without segmenting the street heavily and creating a mess. Temporary disappearance of lanes makes editing complex. Routing ultimately ends up as a lowest common denominator result. The alternative method of representing this same street is as a series of individual lines. But this is equally problematic. In this model, the street loses its coherence as a single entity - humans think of it as a street with multiple uses (walking, cycling, driving, trees). Where people have done this, attributes such as street names need to be kept in sync, and in practice separate pavements often fail to have names attached. Concepts such as the ability to cross from one side of the road to the other (or even switch lanes) are not modelled, with the result that a router may take the user to the end of the street then back down. And cartography ends up showing a series of parallel lines which looks messy and does not match the human perception of a street. The bicycle tagging page on OSM provides a perfect demonstration of the current problem: https://wiki.openstreetmap.org/wiki/Bicycle It shows the complexity of representing many common scenarios, with increasingly incomprehensible tagging combinations. No router implements anything like all of this, and even expert OSM contributors would shy away at bothering to add this data. Cycleways indeed are a good general example of inconsistent tagging. Cycleways separate to a road are sometimes tagged as an attribute of the street and sometimes as a separate geometry. What about a hybrid/stepped cycle lane of the kind seen in Copenhagen - is that a cycle lane or a cycleway? Do lane counts include cycle lanes or not? How is obstructive car parking represented? Is the one-way indication applicable to the cycle lane on the road? And so on. Another example is junctions. Should traffic signals be treated spatially (i.e. represent the location of the traffic heads), or should they be treated linearly so that routing works properly? How should the linear model work accurately when there is only a single geometry for multiple directions? Have a look at the roads around the Arc De Triomphe in Paris - it is completely impossible for a routing engine to work out exactly how many signal delays should actually be attributed based on the presence of the marking of traffic signals in the data: https://www.openstreetmap.org/#map=17/48.87391/2.29536 This talk will discuss these cases, and provide a starting point for discussion on what should be done to improve the situation. As people are ever keener to add more detail to the map, and as more and more mainstream users look to OSM, we have to ask whether the current model is arguably creaking too heavily. Is there a way that we can represent spaces as a set of interconnected flows in some way? The speaker, Martin Lucas-Smith, is one of the developers of CycleStreets, one of the earliest and most established dedicated cycle routing engines. As such, he has spent many years considering the kinds of tradeoffs represented by the current OSM data model.

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This study examines OpenStreetMap data quality at different stages of a participatory mapping process developed for understanding inequalities in healthcare access of informal urban residents in Africa and Asia. Recent studies have examined quality intrinsically and extrinsically. However, in both cases, the data production processes are often not completely transparent to researchers, therefore limiting possibilities for systematic data quality analysis of the processes leading to OpenStreetMap update.

Globally, the lack of detailed quality spatial data of informal urban settings, such as slums, is increasingly becoming a concern to both researchers and development agencies (Hachmann, Jokar Arsanjani, & Vaz, 2017; Kuffer, Pfeffer, & Sliuzas, 2016). One potential for making spatial data available is through Volunteered Geographic Information (VGI) which is opening up new possibilities of data production in recent years and facilitating the emergence of several initiatives aimed at “putting the most vulnerable people on the map” (MissingMaps.org, 2018; Shekhar, 2014). The increasing availability of volunteered and crowdsourced geographic information, in particular OpenStreetMap (OSM), has led to plethora of scientific studies with emphasis on evaluating the quality of the OSM data. The quality assessment results are usually presented in the form of tables, diagrams, map and statistics per given area (Barron, Neis, & Zipf, 2014; Sehra, Singh, & Rai, 2017). Some recent studies have examined OSM data quality without using any external data; the so called intrinsic approach (Barron et al., 2014). In contrast to intrinsic approach, other studies commonly used what is referred to as the extrinsic approach where the OSM data is compared with external datasets such as the UK Ordnance Survey data or National Park Service lists (Haklay, 2010; Zipf, 2017). In both approaches, the data production processes are often not completely transparent to researchers therefore limiting possibilities for systematic data quality analysis of the processes leading to OpenStreetMap update. This presentation examines OSM data quality at different stages of a participatory mapping process developed as part of an ongoing research project focused on understanding inequalities in healthcare access of slum residents in the Global South. The following research questions are addressed: (1) What is the level of spatial data quality one can expect at different stages of the mapping process leading to final update of the OpenStreetMap database? (2) What are the factors influencing quality? Our exploratory method applies recently developed data analytics framework for spatio-temporal analysis of OpenStreetMap History Database (OSHDB) to our study areas vis-à-vis the participatory mapping process workflow. OSHDB framework will serve as a mediating framework to allow flexible analyses of OSM full history data completeness in our study areas. A multi-country case study associated with an ongoing research project of the National Institute for Health Research Global Health Unit on Improving Health in Slums at University of Warwick is used. This Unit focuses on health services in slums through the study of seven slum sites across two continents (Asia and Africa), with the ultimate aim of finding optimal ways to deliver health services to slum dwellers (Lilford, 2017). The historical data sets are derived from the following stages in the participatory mapping process: before online mapping (i.e. digitisation from satellite imagery), after online mapping and validation but before ground-truthing, and, after ground-truthing. The before-and-after estimates at each mapping stage are discussed together with lessons learnt, and feedback, from the project including comments from fieldworkers and supervisors. We thus present initial results from a spatial data quality assessment of the mapping process workflow used to map our study areas and update the OpenStreetMap database.

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A place where Local Chapters can meet with each other

Join the Local Chapters Congress to learn about what other chapters are doing. Even if you are not on a Chapter but are a local organizer, feel free to join! Local Chapters are not-for-profit legal entities to be established within different territories around the world which can act as official local representatives of the Foundation when dealing with local government, business and media. OSMF is an organisation is incorporated in Great Britain. It wants to support the OpenStreetMap project (and other free mapping projects, where applicable) in the whole world. But it can't be everywhere and reach everybody, especially people speaking languages other than English. Local chapters can extend the reach of OSMF, foster local communities, help with legal and financial matters in their respective countries and generally promote OSM in their local areas. Some of these goals (such as handling financial matters or providing a point of contact for government or media) can also be handled by totally independent organisations supporting OSM but not associated with OSMF in any way. But working together helps OSM and makes it clear that we are all part of the same community. For more about Local Chapters, check out [the wiki](https://wiki.openstreetmap.org/wiki/Foundation/Local_Chapters).

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Our data model is impractical. You know that. Even OGC Simple Features are better. Changesets and versions promised easier reverting — is it simple yet? We have added a lot of features to API 0.6 over the past ten years, but should we have? Let's see what went wrong and what we can improve.

People often come to me saying, changesets with huge bounding boxes are impossible to validate. Come on. You know changesets were not meant to group edits by any criteria. Developers look at the data model and derive user experience from it — and it obviously does not work. Every instance of OSM data needs to be preprocessed, converted, filtered, layered, postprocessed and thrown away. We need to stop looking at OSM as a database and start treating it like a map. In this talk I will highlight what's wrong with the current state of API, including both the actual REST API and the server side. Things like topology, notes, GPX traces and stuff: they were coded when the project was small, but the model starts to show its age — and few people know what to do, besides adding more mappers. How come Overpass API became the better API, and what can we learn from it? Changesets should be abandoned by user-facing tools: let's imagine how the mapping, the validation, the undoing of changes would work if we didn't rely on changesets or actually anything API provides. Can we do something to improve data quality right now? Or can we work towards API 0.7, that would help keep the map not only the most complete, but also the most recent? Let's take a step back and imagine how OpenStreetMap should have been working, to make it more fun to work with, while keeping its versatility and simplicity. I have been involved in a couple API 0.7 discussions, made a few tagging proposals and wrote an editor and a change rollback script. That doesn't make me an expert — there are no experts in OpenStreetMap — but it gave me some ideas on how things could be better. Maybe together we will have a clearer path towards the better OpenStreetMap.

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OpenStreetMap provides a lot of valuable information about urban green spaces, but the numerous and conceptually overlapping OSM tags that describe such features lead to spatially heterogenous representations in OSM. We developed an exploratory data analysis methodology to identify locally relevant OSM tags for mapping green spaces in a specific area and compared the extracted OSM features to administrative data to evaluate the level of completeness in regard to urban green spaces.

Urban green spaces provide a variety of important ecosystem services such as micro-climate regulation, increase of biodiversity and the provision of recreational and cultural services for citizens. Thus, they are an important factor for the quality of life in cities (Bolund and Hunhammar, 1999). However, in order to take advantage of these services citizens need to have sufficient information about the location and qualities of nearby green spaces. Within the project “meinGrün” we are addressing this issue by developing a web-based recommendation service which helps citizens find suitable green spaces that satisfy their personal needs. OpenStreetMap (OSM) plays an important role in this project, since it provides a lot of valuable information about urban green spaces such as their location and the amenities they provide (playgrounds, benches, toilets etc.) However, its spatially heterogeneous data quality, especially in regard to the level of completeness, provides challenges for its usage in a recommendation system. Therefore, the integration of OSM data for our purposes requires a prior assessment of the completeness of urban green spaces. The completeness of certain geographic objects is one of the main fields of investigation in regard to OSM data quality. In recent years several studies investigated the completeness of OSM data with respect to the road network (Barrington-Leigh and Millard-Ball, 2017), buildings (Hecht et al., 2013) or land use features (Jokar Arsanjani et al., 2015). Urban green spaces, on the other hand, were rarely the focus of completeness studies. Ali et al. (2016) developed a method to quantify the plausibility of vegetation-related tags being assigned to specific OSM features and Lopes et al. (2017) evaluated the potential of OSM for extracting information about natural local climate zones. Since both of these studies do not explicitly address the completeness of urban green spaces, we developed a new methodology for this purpose. In contrast to buildings and highways, this poses unique challenges due to the variety of vegetation-related OSM tags and the many different forms of urban vegetation ranging from large parks over private gardens to roadside greenery. OSM tags that describe natural objects are numerous and sometimes conceptually overlapping e.g. some features could be tagged as leisure=park or leisure=garden. This leads to different representations of urban green spaces in OSM across different geographical regions. Defining one set of relevant OSM tags to measure the completeness of urban green spaces that can be applied everywhere is therefore not possible. To solve this issue, we developed an explorative data analysis methodology based on OSM and satellite imagery to identify locally relevant OSM tags that indicate urban green spaces. The analysis is based on statistical and graphical methods to evaluate the association between a certain OSM tag and the presence of vegetation. After the relevant tags have been identified, features representing green spaces are extracted from OSM and compared to an administrative data set to assess the level of completeness. As a basis for this comparison, the study area is divided into patches of homogenous land use based on natural and human-made barriers such as the road network, rivers or objects that mark changes in land use (fences, walls, etc.). On this basis, features from both data sets are joined and the level of completeness is assessed using different extrinsic data quality measures. In our talk we will present our methodology along with the results of the completeness assessment for the City of Dresden, which is a pilot city of “meinGrün”, a project funded by the Federal Ministry of Transport and Digital Infrastructure (BMVI). References: Ali, A., Sirilertworakul, N., Zipf, A., Mobasheri, A., 2016. Guided classification system for conceptual overlapping classes in OpenStreetMap. ISPRS Int. J. Geo-Inf. 5, 87. Barrington-Leigh, C., Millard-Ball, A., 2017. The world’s user-generated road map is more than 80% complete. PLOS ONE 12, e0180698. https://doi.org/10.1371/journal.pone.0180698 Bolund, P., Hunhammar, S., 1999. Ecosystem services in urban areas. Ecol. Econ. 29, 293–301. https://doi.org/10.1016/S0921-8009(99)00013-0 Hecht, R., Kunze, C., Hahmann, S., 2013. Measuring completeness of building footprints in OpenStreetMap over space and time. ISPRS Int. J. Geo-Inf. 2, 1066–1091. Jokar Arsanjani, J., Mooney, P., Zipf, A., Schauss, A., 2015. Quality Assessment of the Contributed Land Use Information from OpenStreetMap Versus Authoritative Datasets, in: Jokar Arsanjani, J., Zipf, A., Mooney, P., Helbich, M. (Eds.), OpenStreetMap in GIScience: Experiences, Research, and Applications, Lecture Notes in Geoinformation and Cartography. Springer International Publishing, Cham, pp. 37–58. https://doi.org/10.1007/978-3-319-14280-7_3 Lopes, P., Fonte, C., See, L., Bechtel, B., 2017. Using OpenStreetMap data to assist in the creation of LCZ maps, in: 2017 Joint Urban Remote Sensing Event (JURSE). IEEE, pp. 1–4.

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Osmose-QA is a Quality Assurance tool pointing issues on map, but also able to compare external data set to OpenStreetMap. First part is about the new support of MapCSS rules in Osmose-QA and the opportunity to share rules with JOSM and other tools. Second part is on integration of external data from Mapillary photography objects extracted by AI and from OpenData. It is also on the challenge to deals with lots of OpenData.

At the beginning, Osmose-QA is a Quality Assurance tool pointing issues on map, but also able to compare external data set to OpenStreetMap. First talk part is about the new support of MapCSS rules in Osmose-QA. MapCSS language was primarily designed as way to style map objects, it look like the web styling language CSS. The base of MapCSS is to use selectors to filter objects. JOSM already use it for map styling and validator. This new implementation of MapCSS in Osmose-QA allows to write rules more easily. It also allow to reuse JOSM validator rules set. Next step would be to run Osmose rules in JOSM. It’s a great opportunity to share rules across tools and projects. But come the question on how to share and manage this rules while considering tools particularities. Second talk part is on external data comparison (aka conflation) with OpenStreetMap. It permits to point issues and evaluate the coverage, lastly it allows fixing and integration into OpenStreetMap. The more we wait, the more there is OpenData sets available. How Osmose-QA can try to addresses this issue of already too many OpenData set available. Beside OpenData from authorities we also have street objects extracted by Mapillary AI from street photography. Finally, what about the limit of the current model and how we may go further.

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What does it take to build a new generation of mappers ready to solve challenges within their community? This presentation will feature students from YouthMappers sharing how they are deciding what issues to map, engaging and training new mappers, especially women, and using map data to advocate for change in their local and global communities. Learn how you can plan your next mapping initiative with youth at the forefront.

What does it take to build a new generation of mappers ready to solve challenges within their community? It starts by amplifying the voices of youth, giving them the tools and opportunities they need to learn GIS mapping and understand how they can apply it to their everyday life. It continues by intentionally creating spaces for youth to engage in local and international mapping initiatives. This presentation will feature students from YouthMappers chapters throughout the world sharing how they are building a sustainable, inclusive mapping community at their university and beyond. Each YouthMappers student will spotlight a mapping initiative they are driving or contributing to, sharing their process for deciding what issues to map, engaging and training new mappers, especially women, and using map data to advocate for change in their local and global communities. Learn how you can plan your next mapping initiative with youth at the forefront. YouthMappers is an international university consortium on Mapping for Resilience. The consortium organizes a global community of learners, researchers, educators, and scholars to create and use open geographic data that directly address locally defined development challenges worldwide. YouthMappers’ mission is to cultivate a generation of young leaders to create resilient communities and to define their world by mapping it. We are supported by funding from the US Agency for International Development, our founding universities, and other private donors and partners

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OSM2World is an open-source renderer capable of creating rich 3D worlds from OpenStreetMap data. It is now available as a web application powered by WebGL.

OpenStreetMap contains a wealth of data that can be used to build a 3D representation of our world. Some of it, such as buildings mapped using the [Simple 3D Buildings](https://wiki.openstreetmap.org/wiki/Simple_3D_Buildings) tagging convention, is intended primarily for 3D use cases. Other feature classes, such as roads and railways, urban infrastructure, or landuse information, aren't usually mapped with 3D in mind, but are nevertheless essential ingredients of a 3D world. In either case, unlocking the full potential of OSM data and converting it into a representation that's suitable for typical use cases in the domain of 3D rendering requires specialized software tools. [OSM2World](http://osm2world.org/) is one of these tools. Fully open source, it can be used to export 3D data for modelling software, animations, and games, as a library in other programs, and as an interactive map on the web. Until recently, however, the lack of a powerful 3d rendering solution for the web put constraints on the features available without a local installation of the software. Today, the WebGL standard allows real-time 3D rendering in all major browsers. A new web frontend for OSM2World builds on these capabilities to allow users to explore the world in 3D, based entirely on open data. This talk demonstrates the abilities of OSM2World and its new web frontend, showcases some impressive 3D mapping around the world, and briefly explores how the software works behind the scenes.

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A workshop aiming to introduce Wikidata and how to contribute to it, to explain how Wikidata and OSM are currently connected, and to discuss issues and ways for the OSM and Wikidata communities to further collaborate together.

OpenStreetMap and Wikidata are two of the largest crowdsourced _open data_ projects on the Internet. While OSM and Wikipedia, the more well-known sister project of Wikidata, are often compared with each other (e.g., “OSM is like Wikipedia for maps”), OSM and Wikidata are actually even more similar. OSM has things like nodes, ways, relations, tags, keys, and values, while Wikidata has similar concepts like items, statements, properties, values, and qualifiers. Data modeling discussions on the OSM tagging mailing list are often as passionate as similar discussions on the Wikidata project chat page. Since Wikidata was created in 2012, there have been plenty of links, projects, and collaborations between the two volunteer communities. This includes data links (such as OSM's `wikidata=*` tag and Wikidata's P402 or "OSM relation ID" property), to projects like Sophox which is a SPARQL endpoint that provides access to OSM and Wikidata, and to things like installing the Wikibase extension—the software that powers Wikidata—to the OSM Wiki in order to provide structured data for OSM keys and tags. This workshop is intended to provide participants with an introduction to Wikidata and how to contribute to it, how OSM and Wikidata compare and contrast with each other, and examples of projects or initiatives that combine OSM and Wikidata. The workshop will also teach participants how to use an online tool that lets mappers generate a list of candidate matches between OSM and Wikidata within a specified a place or region, which are then reviewed by the mapper and saved to OSM as wikidata=* tags. The final part of the workshop is intended to be a discussion of ways the two open communities can collaborate with each other including tackling the issues that prevent further collaborations (such as license compatibility).

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As one of the outreach activities in Japan, OpenDatathon which holds mapping party and Wikipedia editing event simultaneously, are regularly held. As a member of an Organizer group, I will introduce OpenDatathon case studies and consider their potential.

In Japan, many OpenDatathon are held by various operators. It does the mapping party and editing of Wikipedia, other OpenData editing as one event. This contributes to the increase in opportunities to learn OpenData, the exchange of people involved in OpenData, and the increase of contributors. As a member of the group that organizes OpenDatathon, I will explain the purpose, effect, and spread of this initiative. The purpose of OpenDatathon's activities is different for each organizing group. Among the purposes, cases aimed at local information distribution are worth noting. It edits information on OSM and Wikipedia and uses it for the development of the local community. Because Civic Tech communities and local government organizations are often interested in it. For example, the library is collecting material about the history of the area. Writing on Wikipedia using them is important to them. The number of OpenDatathon hosted by the library is increasing. We also frequently support events organized by other organizations. That way, they can manage the event without having direct contact with OSM and Wikipedia experts. In addition, it is possible for people of various motivations to participate by incorporating the fun of walking around town. In order to edit OSM and Wikipedia, We are looking for historical or important topics in the local area. In addition, we may hold multiple events as a series of historical themes. We are planning a story in mapping, and creating a motivation for people to participate by incorporating a specific feature. As our future challenge, not only disseminate local information by themselves, they will organize hackathon by using the OSM geospatial database and the Wikipedia document data. We want to increase activities. The code and data are linked in an area-oriented.

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The OpenStreetMap provides probably the most comprehensive digital path network for pedestrians, which had been largely unused in its actual depth of detail so far. Based on findings from the TERRAIN project, this talk will go into detail how this network can be used for roadside-aware pedestrian navigation efficiently, and what challenges and mappings problems occurred. Furthermore, specific needs of various person groups will also have to be considered facing variable data quality.

People primarily move on sidewalks, but this is hardly taken into account in generally available routing solutions. The OpenStreetMap provides probably the most comprehensive digital path network for pedestrians, which had been largely unused in its actual depth of detail so far. Through an efficient and transparent topological extension of the path network, more accurate routes for pedestrians and other means of transportation can now be determined, revealing hitherto unnoticed mapping problems and an undesirable previous focus on automotive mobility. As part of the TERRAIN project, an exemplary publicly available navigation system for blind persons was developed that demonstrates the special challenges of human locomotion based on the OpenStreetMap. Challenges and robust solutions concerning data quality, micro-mapping and transition areas, regional OSM semantics versus mapper interpretations, and path bundling are presented. A large part of the footpaths in the OpenStreetMap are listed in the form of annotations as sidewalks. This well-proven procedure enables a low-maintenance, robust and high coverage of the footpath network. However, a presentation with many paths inevitably leads to contradictions due to path bundling, which requires a more elaborate micro mapping, which is often not noticed by mappers up to now, and can now be shown clearly. But also regional micro mapping can lead to unexpected complications in addition to the increased effort, since a multitude of connection problems can occur which have to be considered. In addition, there are a large number of requirement profiles for children, adults, the disabled or the elderly who travel on foot, in wheelchairs or on scooters. A deeper examination of the map data enables a finer specialisation in different areas of application, which also uncovers and challenges previously unused data treasures and requires special handling in the software.

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In early 2019, Open Data Manchester teamed up with Stockport council, Disability Stockport and Age UK Stockport to crowdsource data around mobility and accessibility in the town centre. This talk will cover the mapping methodology, findings, difficulties and solutions, as well as suggesting ways in which we can better map for these communities, making the data collected for OSM more inclusive and accessible for all.

In early 2019, Open Data Manchester partnered with Stockport Council on a project called ‘Mapping Mobility Stockport’, funded through the ODI Geospatial Fund. Working with Age UK and Disability Stockport, the project crowdsourced data about accessibility issues in Stockport, drawing on the lived experiences of people with mobility impairments to supplement data already available to the council. The municipal environment often includes barriers that exclude people with restricted mobility. These barriers may be the result of natural topography, historic planning, deterioration of the urban environment or planned and unplanned maintenance. Some of the barriers may not be known to the local authority. People with mobility impairments are on the frontline when it comes to negotiating these obstacles, and often have their own knowledge and strategies in negotiating and circumventing them. This is a resource that could be invaluable in helping others who face similar challenges, as well as helping local authorities identify where interventions need to be made. One of the aims of Mapping Mobility Stockport was to begin to map and find ways to continue to share these strategies. Using our Joy Diversion format as a basis, Open Data Manchester co-designed and ran a series of workshops with Age UK and Disability Stockport, which brought together different techniques and mobility strategies contained within the local community to enable people of all abilities to explore and map their environment. We used large, hi-resolution maps of Stockport to scribble on and sketch out routes, before taking to the streets to document, photograph and map the kind of things that these communities come up against on a daily basis. This information was then added to Stockport Council’s mapping system and Open Street Map, helping create a mobility map of Stockport. This talk will explore how we collected this data, sharing some of the findings and insights from the project, such as how what is useful for one disability can be problematic for another (such as tactile paving, which is useful for those with visual impairments but can be problematic for a wheelchair user). The talk will also explore the difficulties that arose out of this when categorising and tagging these features, and question whether more needs to be done in terms of mapping the streets and sidewalk.

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State of OpenStreetMap in Croatia. Community that builds the data, and community that uses the data.

Croatia has no official OpenStreetMap local chapter organisation, but we function well as a unorganised group of organised individuals. We started archiving daily extracts of our country in 2009. and later we extended that task to surrounding countries for daily extracts and some rudimentary statistics generation. Our main goal is to spread the word about OpenStreetMap to our friends, coworkers, tourist information centers, companies, everywhere we can in Croatia and sorounding countries, and in that way get new users and new mappers. There are few tourist maps based on OpenStreetMap data and there are more in development, and there is a growing trend of OpenStreetMap usage for webmaps. Until recently there was a lot of goverment data but it was not available for use, after few requests for the right of data reuse now we have data available for OpenStreetMap use. With goverment institutions starting to grant us the rights to use their aerial imagery, there are privat companies starting to do so too. We acquired not only raster data but started to acquire goverment vector data which is great news. There are a lot of plans, we are moving forward every day one step at a time, but there is not enough of us to do it all ...

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We map because we care to represent the world. Yet maps are never "true", they are shaped by their creators and their circumstances. Map-making is world-making: maps by different authors can give access to different worlds. So how can we make, share, and use maps that are created _by_ these worlds, and not just by a privileged few? How can vulnerable communities influence how they’re represented and affected by our maps?

The popular view is that mapping the world is just an act of reflecting reality and reflecting nature. People rely on map-makers like us to show them the “true” spatial arrangement of the world. And yet, making a map also involves making assumptions, omissions, additions, and generalisations, and it feeds on our training, experiences, and personal perspectives as map-makers. A map is never really “true”, it is shaped by the hands of its creators, and it is always also a product of chance and its circumstances of making, sharing, and use. It can be said that map-making is world-making: maps shape our understanding of the world, and inform our actions in the world, and a map by a different author can give access to a different world. So how do we create a map of the world that is created _by_ the world, and not just by a privileged few? To fully appreciate the complexity of this challenge we need to extend our understanding of what counts as maps and mapping, and why. In order to understand our responsibility as map-makers, we need to become both _critical_ and _caring_ cartographers. This shift in perspectives and attitudes can help us understand better what can happen when our everyday mapping practice connects with real-world circumstances. It may also help us become more responsible as the volunteer workers of OSM. David and Martin will guide you through a careful and caring consideration of OSM based on personal experiences, and talk about the responsibilities we carry as we map and make the world during difficult times. For example, we may encounter circumstances where the OpenStreetMap approach to openness brings complications. Can the communities represented on the platform make decisions about how they’re being written about? Indigenous communities around the world regard their land as sacred and don’t want it to be mapped. Should we as a global community know when not to map? There are circumstances where the act of mapping itself can bring danger to the mapper. Do we have the right methods to assess such risks? What support can we offer those who take them? If we don’t, whose hand gets to shape the map instead? And what does all of this this mean for a do-ocracy, where decisions emerge slowly and can often contradict each other? Or to put it even more broadly, how can we diversify, democratise, denaturalise, and decolonise open mapping? Who cares, you ask? David and Martin have participated in the OSM ecosystem as mappers, researchers, and organisers, and have been involved in the Humanitarian OpenStreetMap Team and other global mapping initiatives. Based on their personal experiences they will introduce you to a careful and caring consideration of OpenStreetMap cartography, and talk about the responsibilities we carry as we map the world. They ask you to consider that maps are not _universal_, and that even in OpenStreetMap many different maps and mappings are possible. They propose that a better map may be a _pluriversal_ one, where many different perspectives can coexist. Can OpenStreetMap accommodate this? Which practices can help us produce maps that represent the world more equitably? More importantly, if we think that mapping can make a difference in a time of global precarity, inequalities, climate catastrophe, and mass extinction, then they ask _you_ to care.

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Pic4Review now helps contributors to integrate open data properly in OpenStreetMap using street pictures validation. Discover the way it works and how this can help improve both OSM and open datasets.

[Pic4Review](http://pic4review.pavie.info/) is a new editor to work on OpenStreetMap using open street-level pictures offered by Mapillary, OpenStreetCam and Wikimedia Commons. You can create thematic missions to work on a specific subject. The features are loaded in the tool, which automatically find available pictures around it, and offers a simple interface to answer questions and editing the features. At start, it was helping contributors adding details on existing objects, like accessibility of crossings, name of shops, levels of buildings... This offers a wide range of possibilities, and was already used by hundreds of contributors to edit around 100000 features worldwide. Now, it also offers to integrate open datasets into OpenStreetMap, the proper way. The complexity of integrating open data in OSM lies in the accuracy of these datasets : we should check if features are correctly located, and properly described. This can be done by surveying, or using freely available pictures of our streets. This is what Pic4Review offers to simplify : import your dataset, create a mission, and work in team to integrate the data which can be verified. This talk will present the problem of open data integration, the Pic4Review tool, and the process of using it for integrating open data.

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New processes to agree on tagging suggestions and their interaction with the editing software available on openstreetmap.org

I instead suggest to collect ideas with deadline 1st of September. I commit to present my ideas then and invite everyone to do so as well (on this list, the wiki, or the forum, other channels if known and openly available). This gives people not attending the conference better chances to get involved. That preparation ensures that we end up with one (or more) deliverables that can be worked on. This is also the reason to prefer the BoF over a panel discussion. I am unable to organize a panel discussion in a way such that it produces a deliverable. Some keywords to foster hope that there is a way forward: - Changeset discussions as a social feature have widespread acclaim - The "Any-Tag-You-Like" principle accompanies and serves OpenStreetMap since more than a decade - IETF's "Rough consensus" (RFC7282) has built the internet - many tools already facilitate to make evidence based decisions, e.g. Taginfo in general and Taginfo's [list of tag usage](https://taginfo.openstreetmap.org/keys/highway#projects)

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**Experience Mapper Only**. Creating presets sometimes become a hassle if you don't have experience reading XML language. There is a simple method to create presets in a simple way using JOSM plugin. This workshop will teach about how to create a presets using JOSM. Note: **This workshop for experience mapper only**

Presets is the most important part when creating an object in OpenStreetMap. Without presets, all the objects will not be rendered in OpenStreetMap site because it doesn't have any attribute on it. Adding presets is one way to add more information in JOSM. Sometimes JOSM only has predefined presets which might give us challenges if we want to add another additional information, such as building structures or building level, because we need to add the key and value through *add* button in the right side of JOSM attribute panel. The solution for this problem is we can create a custom presets by using the text editor in XML language. But, this can become a hassle if you don't have experience reading XML language. There is a simple method to create presets in a simple way using JOSM plugin. This workshop will teach about how to create a presets using JOSM. This workshop will talk about things you need to know before creating a presets, exploring Wiki Map Features, and create a simple presets in JOSM. If you want to attend this talks, please make sure that you already have an experience to contribute in OpenStreetMap. Experience in key and value also good if you want to join the workshop.

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As of today we have OpenStreetMap but it doesn't fit all data and some data is not recommended for submission. We've got user reviews request in OsmAnd and Maps.Me and we would like to collaborate with OpenStreetMap community to create independent open platform for reviews.

I would like to talk about how data could be stored and moderated and would like to explain how that data will be further integrated with OpenStreetMap and will be contributed back to OSM! One of the main contribution to OSM database itself will be justified places and details of places itself. I will explain how tools will validate License sanity and avoid Import problem to OSM i.e. how every edit will be administered by OSM-user. In the end we would like to have seamless OSM user experience integration and create open OSM-satellite project. *OpenPlaceReviews - A new way for local reviews* Open. Collaborative. Trustworthy. Openness - make all the reviews data open, so every project can access and contribute back. Community driven - build a community to make decision about moderation, data structure together. Decentralization - develop a decentralized system from the Day 1 to avoid scalability issues in the future. Trust - create tools to detect fraud, spam & keep all the data open to prevent any data manipulation. Monetization - make business subscriptions once the project is matured and spread the income between applications, authors, moderators and network operators. Further Description will be provided. Further Description will be provided. Further Description will be provided.

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In many regions of the world, a population’s access to essential services is unduly constrained by a lack of proper infrastructure. By performing accessibility analysis using OSM data, we can understand how route infrastructure impacts access to essential services and use that information to inform an intervention. This talk explores accessibility analysis performed to understand the impact of footbridge construction in eSwatini and introduces a python framework enabling users to perform similar analysis.

Bridges over rivers are fundamental pieces of human infrastructure which enable safe crossings for the populations who use them. However, many rural regions of the world lack these critical bridges, and as a result, access to essential services is restricted for millions of people. In pursuit of tackling poverty caused by rural isolation, we have begun experimenting using remote analysis techniques to quantify bridge need and impact. To these ends, accessibility analyses using OSM and other data have proven to be extremely valuable. By comparing a population’s baseline accessibility to its access in scenarios such as a flood or post bridge construction, one can better understand the transportation dynamics of a region and even estimate the number of people that would be impacted with a bridge construction, for example. During the course of running various accessibility models, a python framework tebetebe was built around OSRM [Open Source Routing Machine] which simplifies the process of running different scenarios. This framework is built with footbridges in mind but is generalized so it may be applied to other applications. This talk explores the accessibility analysis process, its caveats and results achieved in the Kingdom of eSwatini. Finally, a walkthrough of the python framework tebetebe is given so that other users may perform similar analyses.

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Have you ever been lost inside a gigantic railway station? SNCF, the french railway company, is developing a pedestrian routing and navigation service to help travelers find their way inside and around railway stations. This talk exposes the challenges and how they have been addressed to provide a robust solution that can handle the great variety of data as well as routing through open spaces.

SNCF, the french railway company, has been using OpenStreetMap to map railway stations for years. Since 2016, travelers can explore all 380 railway stations of the Greater Paris area through interactive maps deployed on their web site and on a mobile app. Browsing a map is good, but not good enough when you are lost inside a complex, multilevel, indoor (and/or outdoor) environment. It gets worse for someone in a wheelchair, elderly people, or anyone coming back from a family trip with more luggage than your hands could carry. What if we could bring pedestrian routing and navigation services to help travelers find their way inside and around railway stations? Could we do it using OSM data? This is the challenge SNCF has been trying to tackle for the past few months. Together with Jawg Maps and Carto’Cité, they held a detailed mapping campaign on 83 stations and developed a dedicated routing engine. This engine can seamlessly navigate indoors and outdoors, and achieves routing through open spaces such as pedestrian areas and station halls. This talk exposes the challenges we faced. It focuses on the data structure and the algorithmic strategies that have been defined to provide a robust navigation service, and its ability to handle the great diversity of railway stations. The talk also discusses how some parts of the data model originally designed for road navigation could be used for pedestrian routing – with some minor tweaks. If you thought mapping stairs was trivial, you would be surprised…

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In this workshop, OSM mappers and software developers Beata and Laura will help you get started adding the more than 2 million roads that are missing from OSM according to ImproveOSM, the global open dataset that compares actual car trips with OSM data.

In this workshop, OSM mappers and software developers Beata and Laura will help you get started adding the more than 2 million roads that are missing from OSM according to ImproveOSM, the global open dataset that compares actual car trips with OSM data. In the first part, we will show you what ImproveOSM is, how it is made, what the data looks like and how you can use it. In the second part, we will get our laptops out and do some mapping using the JOSM ImproveOSM plugin. We will have a leaderboard for the event and mapping prizes as well! The workshop assumes you have some experience using JOSM. Bring a laptop! Beata Tautan-Jancso is a java developer and leads the Telenav JOSM team. She works on implementing different tools that expose the results of the Telenav OSM data analysis team. Laura Dumitru is a java developer that works on services and JOSM plugins that aid in the process of visualizing and validating the results of the data analysis. Telenav has been a user of and contributor to OpenStreetMap since 2013, when we launched the Scout mobile navigation app powered by OSM data. Even before that, Skobbler, now part of Telenav, has been part of the OSM community for much longer. Our contributions to OSM include tools like OpenStreetCam, MapRoulette, MapDust, a number of JOSM plugins to help mappers, as well as ImproveOSM open data to help mappers find missing roads, one-ways and turn restrictions. Since 2015, The Telenav map team has improved OSM by adding thousands of new roads, turn restrictions, and detectable road signs in the U.S. and Canada.

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Norway has deployed OSM on a national level for journey planning and is taking initiatives to expand the user, and usage of OSM in Norway and abroad.

Entur’s mission is to enable and increase the use of public transport in Norway. Entur operates the national registry for all public transport in Norway, collecting data from 60 public transportation operators. The registry contains data about 21,000 daily departures on 3,000 routes. This data is open and free of use for app and service developers. We would very much like to present our success story of using OSM data as the foundation for all interchanges and routing in our national journey planner (used in the app: Entur, and http://en-tur.no), and the surprisingly easy challenges we faced due to the quality of OSM data, and the way we solved the large scale challenges, such as missing roads around the country. Why we are going to continue to build on the usage of OSM, OpenTripPlanner and other open source platforms on a European wide base to create an ever-improving public transport information distribution. We are actively engaging with the Norwegian community to build relations, improve quality and mapping methods, even arranging beer meetups. We are of course providing all our data openly, making it easy for OSM to make good use of for example our national stop place database, or for our NeTEx based time table data to be used in innovative solutions.

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Most of the existing attribution guidance for OSM derived works dates back to 2012 and was written before or around the change of the OSM licence to the ODbL. While there have been relevant discussions and rulings by the OSMF LWG (Licence Working Group) since then, there is no easy way to find it in one document. The LWG has undertaken to review the existing guidance and update it where necessary and is now asking for community input.

Most of the existing attribution guidance for OSM derived works dates back to 2012 and was written before or around the change of the OSM licence to the ODbL. While there have been relevant discussions and rulings by the OSMF LWG (Licence Working Group) since then, there is no easy way to find it in one document. The LWG has undertaken to review the existing guidance and update it where necessary and is now asking for community input.

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Replacing the classics "Continue for about 200 m then turn left" by guidance instructions in more natural language automated from landmarks are still a research topic but aims to allow users to move with more confidence. The objective is to test it on an indoor/outdoor pedestrian route calculator. Anything that can be used as a landmark is extracted from OpenStreetMap, then categorized and classified to annotate the route: relevance, visibility, relative position…

Replacing the classics "Continue for about 200 m then turn left" by instructions in more natural language with landmarks incorporation. It aims to allow users to move with more confidence in and around railways stations. Guidance instructions in more natural language automated from landmarks are still a research topic. Innovation & Research branch of SNCF, French National Railway Company, has delegate to Makina Corpus the task to implementation the state of the art in this field. The objective is to test it and then improve an indoor and outdoor pedestrian route calculator under development. Anything that can be used as a landmark is extracted from OpenStreetMap, then categorized and classified. The landmarks are evaluated to annotate the route: relevance, visibility, relative position.… The process have to deals with indoor and outdoor but also as points or large landmarks. We talk on subjects like evaluate how OpenStreetMap objects are suitable to be landmarks. We also expose how check for objects visibility based only on OpenStreetMap data, or how to allow landmark to be visible from outside a building or room depending on the outdoor or indoor status of the observer. The railway stations context has the particularity to require support of transition between indoor and outdoor.

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'Public Transport Navigation using OpenStreetMap by OsmAnd'

The presentation describes stages of developing and starting of new feature of OsmAnd app.

OsmAnd is the first among the large OSM apps that developed public transport support. Now we continue to improve this function further, add new modes of transport, and experiment with the settings.

How we came to the idea of ​​public transport? In the presentation, we will explain why we use the PTv2 scheme, why this option is preferred, and how it works. In addition, in our report, we will consider the future support of GTFS.

During the work we faced various difficulties, so we will explain what these difficulties were and how we tried to build at least something. We will also cover how OSM users can help us, and whether it is possible and how long it will take to improve the data.

Lastly, we will present our results and plans, namely, what was our most significant success and how we want to build an editor for public transport..

We launched the function at the beginning of the year, and throughout the year we tested it, supplemented it with new conditions for building a route and tried to take into account all possible conditions for building routes. Although the data in the OSM have not proved to be always correct, everything can be fixed by the community.

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Adaptation of GraphHopper (routing and map matching) to the railroad context in order to serve a vast diversity of usages.

Today, OpenStreetMap is heavily used at SNCF (the French Railroad Company). We have implemented our own OpenStreetMap servers with customized tiles, geocoding, reverse geocoding, uMap, OverPassApi and GraphHopper. OpenStreetMap rail data covers the European territory and offers a good accuracy. That is why we had the idea to rely on this great source of information for our internal needs and to go further. GraphHopper is mainly used for car, pedestrian and bicycle routing. But there was a piece missing: train routing. With the help of Geofabrik, the SNCF has adapted this great tool to make it work with OpenStreetMap rail data. This will help us cover a vast diversity of usages such as customer information, delay calculation, distance calculation, positioning in tunnels, high accuracy positioning, map matching, and autonomous trains. The project has been named OpenRailRouting and will remain open source. # Presentation: 1. The Project (5 min) * Why OpenStreetMap? * Why Graphhopper? 2. The transformation (technical part – 5 min) * Used Data * Main Code Adaptation 3. Usages (10 min) * Embedded Real Time MapMatching * Client App (Mobile and Web) * Internal Usage to supervise the trains (Delay calculation) * Positioning in tunnels 4. Questions (5 min)

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OpenMapTiles is an open-source set of tools for processing OpenStreetMap data into vector maps, which can be produced in various coordinate systems.

OpenMapTiles is an open-source set of tools for processing OpenStreetMap data into zoomable and web-compatible vector tiles, which are ready to use in OpenLayers, Mapbox GL, Leaflet, QGIS as well as mobile applications. This talk gives an overview of the OpenMapTiles tools to produce vector tiles in coordinate systems other than Mercator while keeping all the features, such as interactivity to create powerful region-specific applications. Today’s de-facto global standard is a Spherical Mercator projection because it preserves shape and angles. The entire world looks like a square, which makes it easy to work with on a computer. However, such a transformation causes several issues like distortion of sizes where places around the equator are visually smaller than those near poles. That’s why new global coordinate systems were developed. There are also many local coordinate systems with a long tradition. They perfectly fit the need to display a particular country/area in the most acceptable way. They are heavily used, especially by governments. OpenMapTiles can serve as a base map for displaying data in a different coordinate system or for view/print purposes. There is also a hosting service based on OpenMapTiles called MapTiler Cloud, which offers the same ability to be displayed in a different coordinate system than Mercator.

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Closing Session

Closing Session

Tools and Tagging

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The following lightning Talks were sumitted during the conference for this slot: * MAMAPA: *Mannheimer Mapathons – Promoting Integration, Providing Humanitarian Aid* * Alexander Dreidt (Landesstelle für Straßentechnik Baden-Württemberg): *Road Works Management System BW – BIS2.0* * Holger Bruch (Mitfahrdezentrale): *Public transport stops – official (BW) data vs. OSM* * Nick Whitelegg (Solent University): *OpenTrailView 360*

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Lightning Talks

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The following lightning Talks were sumitted during the conference for this slot: * mapmaker daria: *Thinking about Indigenious Mapping* * Disastermappers Heidelberg: *OSM Subnational Boundaries Extraction & Tagging Improvement* * Toshikazu Seto: *mapcompare.jp launch!* * Etienne Trimaille: *Two Ways to Use OSM Data in QGIS: Docker-OSM, QGIS*

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The following lightning Talks were sumitted during the conference for this slot: * Jerry Clough: *Postboxes – Mundane Street Furniture or …* * Thierry Jean: *Open Database of Addresses in Brazil (or World) Inspired on BANO Project (FR)* * Michael Reichert: *OSM Inspector News* * Guillaume Rischard: *How to Map Bus Lines Quickly with Routing Tools and a PTv3 Proposal*