State of the Map 2021

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SotM Working group welcome the OpenStreetMap community to SotM 2021. There will also be some explanations about the virtual conference, the interaction in Q&A and the beside sessions.

SotM Working group welcome the OpenStreetMap community to SotM 2021. There will also be some explanations about the virtual conference, the interaction in Q&A and the beside sessions.

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The common theme for OSM editors is presenting a map for moving things around. But for attribute editing, a map is too distracting. Let's see how getting rid of it improves the editing experience.

The future of OpenStreetMap editing is not another JOSM or another Potlatch. What we have is good enough, and there are options for everyone. But 15 years on, the same problems persist in OSM: while it's very easy to draw things, to add a road, a building, or an amenity, updating the map is so hard virtually nobody does that for free. That can only be fixed by tailoring editors to the task, because general-purpose editors cannot help. That is the future: thematic editors. In this talk we'll take a look at existing specialized OSM editors, like OSM Contributor, MapSwipe (I know), or StreetComplete. We will also note when an editor doesn't need a map to operate. And finally we'll imagine a few editors that could simplify maintaining the map, both on desktop and on mobile. The speaker, Ilya Zverev, has made the well-known Level0 editor which doesn't need a map to function, but also isn't strictly a thematic editor. Recently he's made a Telegram bot for a POI directory, which also featured an optimized POI editor. It enabled Ilya to collect ~500 shops and amenities in 10 days, and to keep the data recent. It didn't provide an interactive map, obviously.

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Grab launched a navigation product for Driver partners in 2018 with the help of OSM. We contributed to OSM data in Singapore and made it navigation ready to enable launching in-app navigation for Grab's driver partners. This talk is a glimpse of the journey to get the map data quality and in turn the product experience to be the best suited one for driver partners in Singapore.

Grab's journey with OSM started in 2015, with the road geometry being the one mostly used to calculate ETAs and routing. Based on this use case, we started enhancing the OSM data using GPS probe based detections and satellite imagery. After the success we saw with the ETA and routing quality that we could achieve with OSM in Singapore, we started dabbling with the idea of piloting Navigation quality maps on OSM. Our journey on this started with researching the local traffic rules, signboards and restrictions that are relevant for Navigation. As Singapore is one of the countries in SEA which is well marked with all signs in SEA, we then moved on to solve the next level of challenge which is adding street level imagery across the country with the help of different cameras we could lay our hands on for the pilots. At times we used mobile phones with KartaView app and others we managed to add using better quality Go-Pro like cameras. Few interesting learnings along the way; Rain can play a spoilsport to your plans at the last minute; Government, building authorities would need time to give you permits for collecting imagery and you will need to plan those into your collection efforts; Roadworks, Road closures, congestion do not let you cover all streets fully. Despite all the planning, sometimes you may not get permissions, Eg: Private condos. For such situations, you can work with the local community to help add more imagery. Interesting learnings while mapping Singapore: Tunnels, Underpasses, etc. criss cross on multiple levels. Oftentimes mappers get confused, despite the availability of imagery. Road name abbreviations are sometimes not standardised and will have inconsistency when showing on maps. Maps are never perfect even in a well mapped country like Singapore as the physical world around you is changing. Instead of trying to be perfect, you can aim to be very agile and responsive, making the real world changes reflect in the map data as close to realtime as you can get. What can help you in doing this well is working closely with local transport authorities (SLA, LTA, etc. in case of SG); Other major realtors and establishments which run several malls, office buildings, tech parks, etc. And last but not the least - community. Both the OSM community and our driver partner community helped us immensely here. The journey to making the map for Singapore was quite arduous and needed the continued support of OSM community mappers, driver partners, local map operations teams. Everyone constantly keeping the map updated and improved helped us make the navigation to be best suited for the needs of Grab driver partners. We engaged these communities through various activities such as Geo*Stars, mapathons with organisations such as GeoWorks and hosted in-house mapathons to enable understanding and contribute to the amazing OpenStreetMap data. For our driver partner community, we started off with a whatsapp group to gather feedback and then proceeded to an in-app collection mechanism to make this reporting of road updates, map changes, a lot faster and seamless. This coupled with our on-ground map operations teams which run road tests of the navigation helped us ensure our map stays as updated as possible. Working closely with the community ensured that they also could see the impact they were making on the maps every day. This made Grab Driver partners to like Grab Navigation even more as it empowered them to build it together with us. This journey doesn’t end here, and we will be working even more closely with the community across South East Asia as this is the best way to scale these efforts on building maps for everyone.

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This workshop will introduce two methods on how to identify unmapped highways in OSM. First by making a comparison between OSM and government (open) data, second by calculating the distance between population and OSM highway data. This method will assist OSM contributors to understand where are the unmapped highway without having to manually check the imagery and what is on OSM. The workshop will be delivered using open-source GIS software and publicly accessible data.

OSM evaluation data has been done by many parties on various aspects, starting from tagging correctness until the geometry accuracy. Another aspect that can be evaluated as well as the data coverage. This analysis aims to evaluate how's the good coverage of OSM data, whether OSM has been covering all the area, specifically on the road network data coverage. A good map data ideally can cover the whole area. Thus, the results from this analysis, hopefully, could help the mapper to get an insight into the quality of OSM mapped road network data and identify where are the unmapped areas. There are two methods offered in this analysis. First, OSM data will be compared with the official road network data from a trusted source, such as a dataset released by the government. Second, OSM road network data will be compared with the population distribution to know whether all the populated areas have access to the nearest road network. In the ideal world, all populated areas should have close proximity to the road network, ensuring them having access to public services such as transportation and public facilities. The closer population to the road network, the easier it is. The analysis will be using open-source GIS software, which is QGIS, and a publicly accessible dataset.

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From remote mapping to OSM-based analytical tools for decision making, CartONG would like to share its experience on building and implementing step by step since 2017 an overall strategy to support humanitarian response in refugee sites with UNHCR. We will focus on specific technical challenges, like dealing with accurate and precise data on a large number of sites at world scale as well as at site level.

CartONG is an NGO specialized in Information Management whose mission is to put data – in particular geographical data – at the service of humanitarian, development and social action projects. CartONG is one of the implementing partners for mapping and information management (IM) projects of the UNHCR, a UN agency dedicated to protect refugees, forcibly displaced communities and stateless people. Getting data on refugee sites has always been a challenge given their fast evolution and the diversity of situations around the world. Therefore CartONG has been promoting OpenStreetMap as a complementary resource and sharing platform to produce accurate and recent data that can complement UNHCR’s existing database while also benefiting other actors (in particular local ones). From remote mapping to OSM-based analytical tools for decision making, CartONG would like to share its experience on building and implementing step by step since 2017 an overall strategy to support humanitarian response in refugee sites with UNHCR. We will focus on specific technical challenges, like dealing with accurate and precise data on a large number of sites at world scale as well as at site level. The implementation is still ongoing and the strategy also evolves as it moves forward to better fit the requests of the partner as well as evolution of the OSM landscape: we will present where we stand and what is planned for the next steps. Step 1/ Improve the consistency of the refugee sites’ tagging model on OSM (wiki documentation/ mapping data model) Step 2/ Promote refugee site mapping on OSM (training/mapathons/data integration/ cooperation among actors) - Improve the availability and quality of OSM data on refugee site around the world - Facilitate contribution and use of OSM data. Step 3/ Build a dedicated OSM replica for UNHCR specific use cases Step 4/ Monitor activities on OSM camp mapping - Complement UNHCR’s database and compensate for lack of data on specific sites. Step 5/ Generate a specific map rendering Step 6/ Feed a web application for spatial analysis - Create analysis products (including maps, sector indicators visualization, etc) using OSM as well as other data sources, and make them available to UNHCR’s team and other actors for field operations

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This talk will focus on the MapaTanda Project (a portmanteau of Mapa -- which means a map -- and Tanda -- which can mean older adult but can also mean remember) and why we need to build an age-friendly society through OSM. This is a project that seeks to improve the number and quality of data in OpenStreetMap that are important and relevant to older adults (senior citizens) and the ageing population (60+ years old).

In a nutshell, MapaTanda seeks to improve both the quantity and quality of data in OpenStreetMap that is important and relevant to members of the older adult and ageing population (60 plus years old) in the Philippines. This involves adding and cleaning features in OpenStreetMap such as nursing homes, hospitals that provide specialized care for the elderly, retirement homes, local offices for senior citizen affairs, community centers and other facilities that cater to or provide perks and services to older adults, etc. These data can then be used by local and national organizations for policy-making, planning, and implementing projects and interventions. SmartCT's goal as an organization is to put citizens and data at the heart of developing smart cities and communities in the Philippines. We believe in an open, inclusive, holistic, and citizen-centric approach to building smart solutions. To do this, we partner with actors such as local government units to ensure that they have access to quality open data that they can utilize for planning their programs and services. We believe in the power of open data and consider OpenStreetMap as a great amplifier of that power. This talk will focus on community building and openness as foundations of age-friendly cities and communities. Our main goals for its talk are below: 1. Recruit new OSM volunteers 2. Engage the older adult population to contribute in OSM 3. build stronger relations with the local and global OSM community as well as foster partnerships with more local government units and national agencies. 4. call to institutionalize openness through policies

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The open-source 3D renderer OSM2World turns OpenStreetMap data into detailed 3D models of the world around us.

The mission of OSM2World is to build a realistic 3D representation of the physical world using open data and open-source software. This is made possible by two trends: The increasing level of detail captured by the OpenStreetMap database, and the growing availability of open technologies for high-quality 3D rendering. Creating 3D models from OpenStreetMap wouldn't be possible without the impressive effort of the mapper community. One of the goals of OSM2World is comprehensive support for the rich landscape of tagging, such as Simple 3D Buildings and Simple Indoor Tagging. Besides the outside and inside of buildings, the rendering displays lane mapping for roads as well as a large number of other OSM features – likely more than any other open-source 3D renderer. In addition to the ongoing work to support a larger share of the OSM data model, OSM2World is being updated to make good use of modern technologies, including physically based rendering (PBR), the glTF format, and WebGL. These make it easier than ever to export visually appealing 3D models for use in many modelling tools or engines, and to display them on the web. This talk introduces the capabilities of OSM2World, with a particular focus on features which were added recently. As such, the session is intended to be suitable for visitors learning about OSM2World for the first time as well as those interested in staying up-to-date about new developments.

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Today’s world of geospatial technology and data is evolving quickly. However, the lives of those living with vulnerability may not be improving, yet are shaped by new technologies. The communities who stand to benefit most from improving technologies, including mapping, are instead increasingly left out of key conversations, opportunities, and developments that center around their lives and their data. This panel will discuss ethical issues around mapping with and for communities living in vulnerability. The panalists are Edoardo Neerhut, Paromita Basak, Innocent Maholi, Rosario Casanova, Erica Hagen

This panel is organized by the humanitarian Community Working Group and Erica Hagen, EthicalGeo Fellow and author of “The GeoEthics in Vulnerability Principles”. The moderator is Courtney Clark, Program Director for Everywhere She Maps (YouthMappers) and Manager of Sponsored Projects for American Geographical Society. The panelists are: - Erica Hagen, Director, GroundTruth Initiative; Founder and Trustee, Map Kibera Trust; EthicalGeo Fellow - Rosario Casanova, President of the Academic Network UN-GGIM: Americas - Edoardo Neerhut, Program Manager, Facebook (Mapillary) - Paromita Basak, Intern, Food and Agriculture Organization Headquarters, Project Assistant, C2M2 Project Bangladesh (Bangladesh Open Innovation Lab) - Innocent Maholi, Co-founder and Executive Director, OpenMap Development Tanzania “Today’s world of geospatial technology and data is evolving quickly. However, the lives of those living with vulnerability may not be improving, yet are shaped by these new technologies. The communities who stand to benefit most from improving technologies, including mapping, are instead increasingly left out of key conversations, opportunities, and developments that center around their lives and their data” (Erica Hagen, “The GeoEthics in Vulnerability Principles”). This panel will discuss ethical issues that should be considered while mapping with and for communities living in vulnerability. Potential lines of inquiry for the panel will include: What is the best practice around balancing a need for good and quick maps and the potential for exploitation? How is ethical mapping with vulnerable populations the same or different from responsible data, or other kinds of ethical frameworks? Who participates in the planning and execution of mapping exercises in vulnerable locations, and who decides who should participate? Who decides what is important, and whose voice is left out? How do we obtain consent from a community, and who provides consent? Who controls the use of information? Who owns the output, the maps, and the resulting data? What is left with those who generated the information and shared their knowledge? In whose interest are projects in vulnerable locations conducted? How do we address privacy concerns related to the use of aerial and satellite imagery, and how can we prepare vulnerable communities? How can we be sure that the benefits of our mapping will outweigh the risks or harms? Questions are from “The GeoEthics in Vulnerability Principles” document.

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OSM publishes with its geodata also meta data describing the contribution process and contributor. This talk gives an overview of the actual privacy prospects for OSM consumers, potential privacy risks for OSM contributors, and attempts a preliminary compliance check with respect to the EU’s general data protection regulation (GDPR).

I am a professional data protection expert and a passionate long-term contributor to OSM. For this talk, I want to combine both worlds and discuss: * 0) How OSM already today is beneficial for the privacy of OSM consumers? * 1) Which personal data is in the OSM public database (spoiler: behavioural data of contributors)? * 3) Which potential privacy risks stem from the data for OSM contributors? * 4) What are the GDPR compliance issues? * 5) What is the outlook? I open the discussion (Q&A) with some ideas to mitigate privacy risks. They involve likely changes to the current data governance, OSM database structure and OSM data itself. Problems that are already evident that I plan to mention: 1. transparency on the processing of personal data of contributors 2. tracking of contributors, e.g. via - [https://resultmaps.neis-one.org/oooc](https://resultmaps.neis-one.org/oooc) - [https://overpass-turbo.eu/](https://overpass-turbo.eu/) with search "user:username" - [https://hdyc.neis-one.org/?username](https://hdyc.neis-one.org/?username) 3. sharing of OSM data with third parties, see [https://wiki.osmfoundation.org/wiki/Registered_data_controllers](https://wiki.osmfoundation.org/wiki/Registered_data_controllers) For the purpose of the discussion, I want to introduce the audience to a few core data protection concepts: - purpose limitation - data minimisation - definition of personal data in the GDPR - concept of anonymous and pseudonymous data

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Nominatim, the well-know OSM search engine, has recently received a major update of the algorithm that computes addresses from the boundaries and places in OSM. This talk first introduces the new algorithm, explains the background and how it translates to tagging. We'll then take a step back and explore the larger picture what the evolution of tagging scheme of OpenStreetMap means for the users of the data.

Nominatim is the search engine that powers the search box on the main OSM site. One of the fundamental steps of preparing OSM data for searching is the extraction of information about their location, commonly known as their address. This is not a simple task because OSM data is much less structured than traditional databases and many users revert to using external data for this reason. However, the data is there and should therefore be usable. In the course of the last year the address algorithm of Nominatim received a major overhaul to improve how addresses are generated from OpenStreetMap data. The first part of the talk introduces the new algorithm and how it came to be. We'll look into the current state of tagging of boundaries, the problem of the urban/rural divide and the difficulties of country-specific mapping. The second part of the talk deals with the more general question what the evolving tagging schema of the OpenStreetMap database means for data users. The free-form tagging is one of the big strengths of OpenStreetMap. But the lack of rules does not necessarily have to mean lack of order. Using the example of address extraction, I'd like to discuss the future of the tagging schema from the point of view of a data user.

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MapComplete is a newly created, easy-to-use web editor. In this talk, the developer will talk about the editor, how it came to be, how it already performed and what could possible come - especially with your help.

MapComplete is a newly created web editor which aims to be really easy to use. It tries to be just as simple as StreetComplete while offering per-topic views and presets just as MapContrib. In this talk, I present the editor I've made. I will talk about the editor itself, how it grew in the past year and a half, some technical background and give some statistics on edits made with it and which themes proved to be interesting. I'll also touch upon the possibility to create your own theme (but I'll do an Q&A-session specifically for that). Furhtermore, a comparison with other editors in the OSM-ecosystem will be made and differences with StreetComplete and MapContrib will be highlighted, as those two editors were key in inspiring this editor - yet there are some important differences with MapComplete in it's goals resulting in a fundamentally different approach and different use cases. I'll also zoom in on how the user journey is and how a few small features become available when the time is right for them to appear. Finally, I will have a look to where MapComplete could be headed and which projects and technical innovations will be made in the future. If you already want to try the editor, give it a shot at mapcomplete.osm.be

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Experts estimate that only 2-5% of OSM contributors are women. Panelists will discuss how structural inequalities and individual instances of sexist and misogynistic language and behavior present significant barriers to women’s participation, along with steps that organizations, boards, communities, and individuals can take to be anti-sexist members of the OSM ecosystem. This panel is organized by the Everywhere She Maps program of YouthMappers. The panelists are Hanna Krüger, Chomba Chishala, Dara Carney-Nedelman, Marcela Zeballos

Experts estimate that only 2-5% of OSM contributors are women. The results of the 2021 OSM Foundation survey starkly remind us of the drastic gender gap within the project as well as the vicious sexist, racist, and white supremacist attitudes that exist among some individuals within the “community”. While it may be convenient to believe that data are neatly objective packets of information, and that an edit to OpenStreetMap is the same no matter the gender identity of the editing person, countless historical and present-day examples provide clear evidence that data cannot be decoupled from human bias and perspective. At the same time, it is highly encouraging that courageous and innovative groups and members of the OSM community have taken bold steps to increase the participation of women and other minorities. Panelists will discuss how structural inequalities and individual instances of sexist and misogynistic language and behavior present significant barriers to women’s participation. Women experience these inequalities differently depending on their context, privilege, and background, and the panel will address these issues from an intersectional lens. Panelists will also recommend steps that organizations, boards, communities, and individuals can take to be anti-sexist members of the OSM ecosystem. The panel will be moderated by Maggie Cawley, Executive Director of OSM US. The panelists are: - Airin Akter, Everywhere She Maps Regional Ambassador, YouthMappers - Hanna Krüger, member of the German OSM chapter and the OSMF Microgrants Committee - Anisa Kuci, member of cOSMopolIT and OSMF LCCWG and Project Manager of OSM for Wikimedia Italy - Chomba Chishala, Outreach Ambassador, YouthMappers This panel is organized by the Everywhere She Maps program of YouthMappers.

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Makina Maps is a new stack to produce vectors tiles on request from updated OSM database. The same thing we except from Mapnik, osm2pgsq and mod_tile stack, but for vector tiles.

The Vector Tile is a mapping solution where the Data Vector Tiles are served separately from the style and only assembled eg. in the web browser. Makina Maps is new Vector Tile Server build using Docker and based on Imposm, OpenMapTiles, TileServer GL and NGINX. The stack can be easily and quickly set up. The components allow to import OSM data into a Postgres database and server tiles on request. The tile caching is included. Using Imposm the OSM data can be update and the tiles cache invalided. This stack can be used as tile server for on request query and support fast update after many improvement was done and still in progress to OpenMapTiles. Fist, it requires to speed up the OpenMapTiles data layers query to server pretty quickly new tiles while users browsing the map. Secondly, it need to be able to update the database as fast the OSM diff update are coming and without locking the database. In complement of building and serving vector tiles, Makina Maps can hosts vector tile styles and is able to build and server raster version of these tiles. There is also the possibilities to server stored tiles from MBTiles like raster or eg. RGB dynamics light hill shading.

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This track gathers various lightning talks of 5 minutes each.

## Building a Free Worldwide Long Distance Hiking Trail Map Together With OpenStreetMap *Davey Lovin* | *[mDav](https://www.openstreetmap.org/user/mDav)* Dozens if not hundreds of users worldwide have contributed a wealth of long-distance hiking trail information to OpenStreetMap. However, this data is difficult to extract in a meaningful and useful way and its quality varies greatly. This talk introduces [superroute](https://superroute.org/): an interactive webmap and quality assurance tool for OSM long distance hiking trails, with the goal of coalescing a community of passionate data-nerdy hikers to maintain and improve the data quality of these trails for the benefit of all. ## OpenStreetMap in the Philippines 2021 *OSM Philippines / Feye Andal* | *[feyeandal](https://www.openstreetmap.org/user/feyeandal)* Last September 2020, the OSM-Philippines community released the [Call to Correct Narratives about Geospatial Work [in the Philippines]](https://wiki.openstreetmap.org/w/images/a/aa/A_Call_to_Correct_Narratives_about_Geospatial_Work.pdf). We’re provided with an opportunity to share our own narratives and showcase the local community’s initiatives through a documentary-video produced by Amazon Web Services – Philippines. We would like to share OUR story with the whole OSM community. ## Localizing Community Support through regional hubs. *Geoffrey Kateregga* | *[Kateregga1](https://www.openstreetmap.org/user/Kateregga1)* The Eastern and Southern Africa Open Mapping Hub will engage with local mapping communities, facilitate knowledge exchanges, distribute funding, and provide training and support in order to massively scale local edits to OpenStreetMap in 22 countries. ## Automatic building detection with ohsome2label and Tensorflow *Hao Li* | *[leebob](https://www.openstreetmap.org/user/leebob)* In this talk, we will introduce our recent work in detecting OpenStreetMap missing buildings and show you a walkthrough on how to train your own building detector using ohsome2label and the TensorFlow Object Detection API. ## ORS Tools - the QGIS Plugin for the openrouteservice *Jakob Schnell* | *[ezelo](https://www.openstreetmap.org/user/ezelo)* QGIS is great geoinformation software, the openrouteservice is a fantastic routing engine based on OSM data and this plugin brings them both together. Jakob Schnell from HeiGIT, one of the plugin maintainers, will give a short overview over the main functionalities of the plugin. Please talk to Marcel Reinmuth during the conference, since Jakob won't be able to attend :) ## OpenStreetMap in support of UN Peacekeeping missions: Unite Maps & UN Mappers *Michael Montani* | *[Michael Montani](https://www.openstreetmap.org/user/Michael%20Montani)* [Unite Maps](https://geoportal.un.org/arcgis/apps/sites/#/unitemaps), a program led by the United Nations Global Service Center, is leveraging OpenStreetMap data to deliver geoservices and cartographic products to UN Peacekeeping missions in Africa. The initiative supports peacekeepers in their operational efforts (as navigation and security) in areas torn by conflict. Unite Maps contributes to the extraction of OSM data as well as community support and capacity building of OSM communities under the umbrella of the UN Mappers network. ## Water and sanitation mapping in Nairobi's informal settlements. *Peter Ageng'a* | *[Peter Agenga](https://www.openstreetmap.org/user/Peter%20Agenga)* In this talk, we'll be sharing about the project that we conducted on mapping of water and sanitation in informal settlements of Nairobi through the support of OSMF under the 2020 microgrants projects. We'll be sharing some of the results and lessons learnt from the project and findings from a survey that we conducted on the impact of Covid on water and sanitation.

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Over the past year, the Mapbox Data RAVE team has worked to improve data quality issues and engaged with various communities according to the Organized Editing Guidelines. The experience has been great and varied significantly from place to place. This talk will share the details and learnings for the community and other editing teams.

In October 2020, as part of on-going work to improve the quality of OpenStreetMap data, Mapbox Data RAVE employed osmlint-osmium and osmlint to detect road network data issues in Germany, France, Belgium, Netherlands, and the USA. We found significant issues that impact auto navigation, and variance depending on past editing activities of the community and corporate members in each country. We began our work in each place in conversation. Sometimes we were met with friendliness, other times skepticism. We learned to respect local language conventions, which sometimes meant using machine translation, and this did help greatly. The preferred and active communication channels varied as well, from mailing lists, to forums, to IRC and Slack, to the Wiki and GitHub issues. For documentation, we took an “all of the above” approach, making sure to reflect adaptations in approach in all the spaces. Technically we worked on 10 categories of mistakes: crossing highways, crossing highway bridges, impossible angle, island highways, impossible oneways, mixed layer and 4 categories connected with turn restrictions. We used all available ground truth sources to determine if an edit was needed, and tracked those edits with a changeset comments hashtags for our team and each country. So far we have reviewed more than 26,000 issues and fixed almost 55% of them. Hoping that this is just the beginning of coordination with communities and the Data RAVE team.

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Hear members of the Local Chapters & Communities Working Group share how they are collaborating with their local and federal governments. The panelists are Maggie Cawley (OSM US), Jez Nicholson (OSM UK), Joost Schouppe (OSM Belgium), Stefan Keller (OSM Switzerland), Eugene Villar (OSM Philippines), Naveen Francis (OSM India), Anisa Kuci (OSM Italy) Moderator is Allan Mustard (OSMF Chair)

This talk will feature members of the Local Chapters & Communities Working Group from OSM United States, OSM United Kingdom, OSM Belgium, OSM Switzerland, OSM Philippines, OSM India, and OSM Italy discussing collaborations with their local state and federal governments. Learn how OpenStreetMap is being used by governments all over the world! We will share lessons learned, our ideas for how governments can better integrate with OSM, and success stories from around the globe. We intend to include as many speakers as we can fit into our slated time slot to share experiences from a diversity of places. Are governments mapping road in OSM? Importing buildings? Adding addresses? Or maybe comparing data in OSM to keep their data base current? We hope to inspire other mappers to advocate for OSM in their local communities and even with their federal governments.

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Improving OSM data quality and coastal community resilience with the Map Quality Measurement workflow.

Over the past few years, the world has been experiencing the impacts of climate change with an increase in extreme weather events. Coastal communities are among the most vulnerable and face a range of unique flooding hazards including storm surge, wave impact, and erosion causing damage to homes, businesses, and infrastructure. Timely emergency response relies on high quality spatial datasets to support 911 calls, disaster planning, and response & recovery efforts. It is imperative that road network and water feature (ocean and inland) quality be as accurate as possible when used to support emergency operations. Our team has selected a few coastal cities to run a subset of relevant map error checks to identify the location and density of key errors that would impede response activities. With these data our team uses the Map Quality Measurement process to generate a heat map and narrow down the most problematic areas for communities to focus and improve data.  Map Quality Measurement (MQM) is an analysis and visualization tool revealing the distribution of errors within a given geography. MQM works by running a series of checks, referred to as Atlas-checks, that identify geometric, topologic, and attribution errors. Atlas-check outputs show the density of data errors, the types of errors they are, and assign priority to critical fixes. The checks are written to review core map features such as roadways, buildings, waterways, coastlines, and their relationships with one another.

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Our proposed workshop aims to explore sound maps using OpenStreetMap data and free and open-source software (FOSS) through MANILAud: Metro Manila Soundscapes (http://bit.ly/manilaud; http://bit.ly/manilaudday) The 60-minute workshop will be about the project and also enable the participants to experience submitting soundscapes and hearing their soundscapes on the map.

Our proposed workshop aims to explore sound maps using OpenStreetMap data and free and open-source software (FOSS) through MANILAud: Metro Manila Soundscapes ([http://bit.ly/manilaud](http://bit.ly/manilaud); [http://bit.ly/manilaudday](http://bit.ly/manilaudday)) MANILAud: Metro Manila Soundscapes is an interactive map that aims to help understand how people interpret sounds around them and the noise in their local area through soundscapes; to also help shape local noise policies and plans. Aside from this, it also wants to enable users to explore different areas in Metro Manila and somehow get the feeling of being there in that particular place despite the lockdowns and isolation caused by the COVID-19 pandemic, through the semantic descriptions of the uploader, the soundscape attached to the location, and a photo related to the location. In the workshop, we will be sharing about the project and also let the participants experience submitting soundscapes and hearing their soundscapes on the map. Proposed Workshop Flow (in minutes): * 01-15: Presentation about the Project (MANILAud: Mapping Soundscapes Through Participatory Data Collection - A case study of Metro Manila) * 16-20: Workshop instructions for submitting soundscapes * 21-35: Workshop proper: for participants to submit their soundscapes * 36-45: (Participant's Break) to allow the team to add soundscape submissions on the map * 45-55: Listening to the soundscapes on the map * 56-60: Questions/Feedback from the participants

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How to remotely map a city's public transport when due to the pandemic local trainings and community buildings are not possible.

Financed by the World Bank who wants to support the infrastructure of Mauritania and also the planning of the bus system, we from Trufi Association wanted to map the public transport of its capital Nouakchott. Our typical approach would be to train and empower local communities so that they can work with OpenStreetMap and public transport mapping in the long run, keep it updated, ensure ground truth, make it more sustainable. We would have made the mapping together and this way taught how to trace and upload bus lines, bus stops, and points of interest. The plan was to do this together with French organization “Les Libres Geographes” who are experts in this topic. Due to Corona, we were not allowed to enter the city and had to think of a plan B. We will show in this talk how a local team can do the collection of necessary data and how a remote team can do the mapping – organized by a regional partner who at least is nearby. We show how both sides could benefit nearly as much as if we had been there, and why this is in times of CO2 reduction and pandemic might be a wise choice for other cities and mapping projects as well. But we also show the limitations, for example that this would not have worked without a minimal preparation of the local people during the last years.

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The mapping of access restrictions is often neglected, but access restrictions (and default assumptions about whether a path without any such information is usable or not) are important to make OSM safe to use.

OpenStreetMap has grown from a geeky niche project to a respectable data source powering a multitude of apps and web sites. Our ethos is to “map reality”, and our approach is incremental – one person might trace something from aerial imagery, another person might add a street name from their own local knowledge, and a third person surveys and records a speed limit on a visit to the area. Many paths, trails, and streets have access restrictions that may not be apparent from aerial imagery. These could range from oneway traffic rules to restrictions on certain types of vehicles, to “no dogs allowed”, or something could be entirely private, or in a military danger area. End users of OpenStreetMap based services are often insufficiently informed about access restrictions, leading to a rising number of complaints from land owners or otherwise responsible individuals, and frequent requests to “immediately remove from OSM” a certain object. This can either be due to missing access restrictions in OSM, or due to existing access restrictions not being correctly interpreted by the platform using OSM data. This talk intends to take stock of the current situation in OSM and major data-consuming apps, and make some recommendations aimed at both mappers and data consumers in order to reduce the risk of leading users down the wrong path. The author is a member of OpenStreetMap’s Data Working Group which handles incoming complaints.

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We have developed an open source web app, OLMap, to take photos of entrances and map them in OSM and another one, Gatesolve, for delivery drivers to find the route to the right entrance based on this micromapped OSM data. Last year, over 6000 images including 5762 entrances, 1767 flights of steps, 645 barriers and 241 info boards were contributed to the 30000 entrances mapped in Helsinki, Finland. The mapping project will continue this summer.

True door-to-door navigation requires us to accept that people don't really visit housenumbers, they visit amenities and apartments via specific building entrances. The significance of this difference varies and is greatest in cities with unoptimal address schemes, for people with accessibility needs and for delivery drivers quickly visiting lots of unfamiliar recipients. City of Helsinki in Finland uses OLMap to take photos of each entrance to a building, to correct the GPS location of the entrance, to record the address and other properties of the entrance as well as to facilitate mapping this information and the access paths in OSM. Last year, the city employed some secondary school students as mappers. Over 6000 images including 5762 entrances, 1767 flights of steps, 645 barriers and 241 info boards were contributed to the 30000 entrances mapped in Helsinki. A new batch of students will be employed this summer. Using Gatesolve, delivery drivers can take advantage of the address, entrance, and accessibility data collected and mapped in OSM. To limit visual clutter, the app includes zoom-dependent cartography of the entrances. The search finds entrance-level addresses and the routing function visualises any steps and barriers on the way to the entrances. An entrance typically doesn't have a unique housenumber, so it's important to also map tags such as "addr:unit", "addr:flats" or "ref" when appropriate. When these are not available, the different entrance types, access values etc. can help distinguish between entrances. If a path without stairs has been mapped through to an entrance, we assume there are no stairs, but if there is any gap between the path and the entrance, we communicate uncertainty. Entrances inside as opposed to along building outlines cause troubles for us. For example, there are entrances inside building passages and under hanging roofs. Mapping these entrances along "building:parts" can clarify this, but it's disproportionately laborous as well as prone to topological mistakes which result in inaccurate cartography. We continue to develop the apps. For instance, all the mapping this far has been done in the iD editor, but custom mapping functionality may be necessary to feasibly support explicitly linking amenities to corresponding entrances via the proposed "associated_entrance" relations.

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Turkish OpenStreetMap Community has constantly been growing with members from various circles in recent years however the community faces various problems such as insufficient OSM documentation and tools in Turkey and lack of learning materials in Turkish. Yercizenler and Youth Season NGOs kicked-off the Open Source Volunteering Programme to tackle community needs to build and enlarge OpenStreetMap Turkey community by training volunteers, translating OpenStreetMap related documentation/tools and mapping 120 unmapped towns in Turkey.

The Turkish OpenStreetMap Community has constantly been growing with members from various circles in recent years. It consists of participants such as hobby cartographers, academics, local governments, university student clubs and local NGOs. According to OSMstats, the community, which has an average of forty active mappers daily, has added more than 1.3 million buildings and more than 200,000 km of highways to the OpenStreetMap since 2018. Active OpenStreetMap contributors identify community problems and needs are listed as following; language barrier and the inexperienced local community. According to the EF English Proficiency Index, Turkey ranked in 79 out of 100 countries/regions. Lack of having OSM documentation in Turkish is one of the biggest barriers to growing a mapping community in Turkey. Insufficient OSM documentation in Turkish adversely affects participants of all levels. It causes a lack of technical knowledge about OpenStreetMap data structure among experienced mappers. Even if they make an outstanding amount of contribution, it can cause a decrease in data quality. From the newcomers' point of view, it significantly reduces the pace and motivation of learning. In order to tackle these problems and needs, Yer Çizenler and Youth Season NGOs are teamed up and kicked off the Open Source Volunteering Programme. The main goal of this programme is building and enlarging the OpenStreetMap Turkey community. The objectives of this programme are to train volunteers to get to know more about OpenStreetMap, to translate OpenStreetMap related documentation and tools and to map unmapped towns in Turkey. This programme consists of three phases; training, translation of OSM tools and mapping unmapped towns in Turkey. 8 OpenStreetMap training, four mapathons, three online public experience sharing events and 18 weekly meetings were organized during the programme. Twenty volunteers were selected to be part of the Open Source Volunteering Programme. These volunteers were trained on the following topics; fundamental of OpenStreetMap, OSM data model, OSM tag schema and OSM editing tools such as iD Editor, JOSM, Vespucci, GoMap! and RapiD. With the workshops and hands-on webinars, the participants got familiar with OSM data structure and gained the ability to use OSM editing tools. Additionally, the Open Source Volunteering Programme helped the Turkish OSM community to be connected with international OSM communities to learn from their experiences. As an example, we hosted Geoffrey Kateregga, Community Programs Manager at HOTOSM shared his learning and valuable community growth experience in Uganda as well as in Africa (1). As the second phase of this program, the volunteers translated OSM iD Editor Vespucci, MapRoulette and uMap tools into the Turkish language. In the third phase of the programme, volunteers mapped 120 unmapped towns in Turkey. Unmapped towns are exported from Pascal Neis's Unmapped Places of OpenStreetMap (2). These towns are mapped as points but do not have any roads in a radius of 700m. The MapRoulette and OpenStreetMap iD Editor are used to map unmapped towns. MapRoulette task was used to coordinate collaborative mapping efforts and monitor the progress (3). As a result, 11.838 map changes were made by 15 mappers. There are 10.295 unmapped towns in Turkey. As Yer Çizenler and Youth Season NGO, we're aiming to enlarge the active OpenStreetMap Turkey community and complete a map of unmapped towns in Turkey by the end of 2021. Overall, this talk will highlight key elements of building and enlarging OpenStreetMap communities in developing countries and learnings from Open Source Volunteering Programme. The proof of concept of mapping unmapped towns methodology will be shared with other communities to encourage them to implement similar activities in their local community. * (1) [https://www.youtube.com/watch?v=fD4fvmJ3Wmk](https://www.youtube.com/watch?v=fD4fvmJ3Wmk) * (2) [https://resultmaps.neis-one.org/unmapped#5/47.100/9.800](https://resultmaps.neis-one.org/unmapped#5/47.100/9.800) * (3) [https://maproulette.org/browse/challenges/15901](https://maproulette.org/browse/challenges/15901)

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Ask the OSMF Board Almost Anything About our Agenda, Actions And Activities

This is a chance to ask the OSM Foundation Board questions, to engage with the board. Let's have a conversation about the Foundation, the Board and how all the parts work together. If you know nothing about what the board is doing, this is a chance to find out. Find out what the Foundation does and doesn't do, what it can and can't do. Find out how you can help, how you can get involved. The Board is committed to openness and wants to engage with the community. We will take questions from the audience, or other questions that people can submit before the event, and we will talk about and answer them. We did a successful AMA (Ask Me Anything) on reddit within the last year which garnered 400+ comments, another source for questions & answers. We can talk about the past actions of the board, and what future plans we have. Are you curious about running for the OSMF Board? This is a chance to find out what is actually involved, to find out what sort of work is done, and whether you would like to get involved. Do you have an idea for something the board could, or shoud do? Here is a chance to suggest it, and talk about it.

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This track gathers various lightning talks of 5 minutes each.

## The Boundary Puzzle *ark Arjun* | *[arkarjun](https://www.openstreetmap.org/user/arkarjun)* The story of OSM Kerala community in developing the administrative boundaries of Kerala, a southern state of India. The Mammoth effort of volunteers made the different level of administrative boundaries of the state created a large impact in recent times including the pandemic management. The talk gives a brief on how the citizen-oriented participatory mapping revolutionised the open data culture and influenced the authorities. ## The YouTube community of OpenStreetMappers *Gregory Marler* | *[LivingWithDragons](https://www.openstreetmap.org/user/LivingWithDragons)* Let's take a look at YouTube and the community there from an OpenStreetMap view. I quickly cover the big and important channels that host talks, provide tutorials and learning, along with those doing desktop streams or turning on a camera and chatting as they go out mapping. ## Let's meet at an island. OpenStreetMap as a source for spatial chat tool worlds *Helga Tauscher* This talk shows how worlds for adhoc video-conferencing systems or spatial chat tools, for example [WorkAdventure](https://workadventu.re/) can be populated from geospatial or other real-world data on an automated basis. The island scenario is the first of three scenarios to be investigated, with the other two being scenarios on the building and city district level. ## Experiments in P2P tiles *Iván Sánchez Ortega* | *[ivansanchez](https://www.openstreetmap.org/user/ivansanchez)* An exploration on how a seemingly obscure web standard (WebRTC) can be exploited to provide P2P transmission of rendered map tiles and theoretically lower the load on the OSMF's tileservers. ## Open Healthcare Access Map *Marcel Reinmuth* | *[maze2p0](https://www.openstreetmap.org/user/maze2p0)* Get insights on travel time to healthcare facilities, population coverage and their interplay at different scales for a variety of countries. The [Open Healthcare Access Map](https://apps.heigit.org/healthcare_access/#/) is an OSM data powered application to explore health access. ## Bye Bye, Unclassified *Martijn van Exel* | *[mvexel](https://www.openstreetmap.org/user/mvexel)* Many words have been said and written about road classification in OSM. In this talk, Martijn van Exel will look at this topic through the lens of recent community initiatives in the United States, and add his own opinions you never asked for. ## Project OsmAPP.org *Pavel Zbytovský* | *[zby-cz](https://www.openstreetmap.org/user/zby-cz)* For a long time, I missed the single application for using OpenStreetMap. In this talk I would like to present the universal OpenStreetMap app, OsmAPP for short. After a few years of development, it already offers fast vector maps, clickable POIs, search and even basic editing capabilities. Let's hear the story of this project and discuss the future. - Link: [osmapp.org](https://osmapp.org) - Slides: [github.com/zbycz/osmapp-talk](https://github.com/zbycz/osmapp-talk)

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A deep dive into how StreetComplete stores edits and syncs them with OpenStreetMap, including persistence, solving conflicts and allowing users to revert their edits.

StreetComplete is known as an app to easily contribute selected data to OSM on the go. The simple interface could make it appear that the technical implementation is equally simple. It is maybe a common misconception that things that look easy to the user are also lighter in terms of code complexity. But under the hood, it is anything but. For example, amongst other things, the app has a unique way to avoid and to automatically resolve conflicts when uploading data to OSM. For an app that promises its users to work completely offline and thus automatically stores unsynced edits for any duration before upload, this is very useful to have. Furthermore, it comes with the ability to undo edits in any order (not just the last) and even undo most edits after they have been synced with OSM already. This talk shall give you an architectural overview over how StreetComplete enables its users to do these things. It could serve other editor developers as inspiration, however, most technical concepts in this app are probably not well applicable to be used in a general OSM editor, as StreetComplete vastly limits its users what they can do and therefor we can make certain assumptions about how the app is used.

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Martijn van Exel will demonstrate in an interactive workshop how you can make use of MapRoulette's advanced capabilities to create tasks that mappers can solve with as little as one click.

What if you could combine the best parts of data imports, bulk edits and the power of the OSM community? MapRoulette lets you do this with two new Challenge types: Tag Change Challenges and Cooperative Challenges. With a Tag Change Challenge, you can propose one or more tag changes on existing OSM features. Mappers simply need to confirm the changes from right within MapRoulette. With a Cooperative Challenge, each Task is a pre-made OSM Change that gets pre-loaded in JOSM, where mappers can easily confirm the edits. In this Workshop, MapRoulette creator Martijn van Exel will show you how these Challenge types work, and how you can create them.

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The osm2pgsql has been around for a long time. Since 2006 it is used to import OSM data into PostgreSQL/PostGIS databases for rendering and other uses. In the last years there have been a lot of improvements to osm2pgsql. With the new "Flex" output, osm2pgsql is now much more versatile which allows new applications.

The osm2pgsql has been around for a long time. Since 2006 it is used to import OSM data into PostgreSQL/PostGIS databases for rendering and other uses. In the last years there have been a lot of improvements to osm2pgsql. With the new "Flex" output, osm2pgsql is now much more versatile which allows new applications. The talk will be about the new developments in osm2pgsql und show how to use the new features. Osm2pgsql is used for rendering bitmap tiles for many maps, including the main OSM map and many others. But it can also be used to import OSM data for generating vector tiles and for many other use cases. It is now possible to tell osm2pgsql exactly what output tables with what fields to create in the database. Transformations from the OSM tags to more or less any format you need in your database can be defined in Lua code. Together with the powerful SQL query language in PostgreSQL and the geometric algorithms in the PostGIS plugin, this makes all sorts of analytical processing possible. Osm2pgsql can be used to import small OSM extracts quickly for one-off processing or run a minutely updated planet-wide database on a reasonably-sized machine making OSM data processing accessible for everybody.

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Since moving to Ireland from Germany in December 2016, I have been mapping heritage features on the island increasingly. During lockdown, I started a YouTube channel to enable other people who are interested in Irish history to learn about OpenStreetmap, because it is still quite unknown in those circles.

After moving to Ireland and shortly after becoming a member of a local historical society, I started mapping historical features like ringforts, castles, church ruins and graveyards. As a member of that group, I noticed that the awareness of OpenStreetMap and OpenData is very low in this group and in Ireland in general. One of the excuses often given by these groups is the demographic which is less computer literate than my generation. However, after uploading a few OSM tutorials onto YouTube, I received good feedback and two female acquaintances of that age group started actively contributing to OSM. Motivated by those developments and by looking for something productive to do during the third COVID lockdown, I started a YouTube tutorial series on OSM, uMap, overpass turbo and fieldpapers to show the benefits of OSM to historically interested individuals and groups. Within only a few weeks, I had gathered a small following and continue getting positive feedback from people who discover OpenStreetMap for their personal or group projects. I believe that giving the public concrete application examples of OSM rather than trying to convince them by dropping terms like OpenData and OpenSource into the conversation, is a way to promote OpenStreetMap to an audience that is keen to learn and to apply the knowledge. It is a time consuming process, but necessary for a shift in awareness about OpenStreetmap.

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This panel focuses on the challenges contributors face in building OSM communities in the South and the strategies they adopt to achieve their goals. It brings together active members of selected OSM communities who will share their experiences and make recommendations for building strong OSM communities. Panel guests are Amadou Ndong, Tshedy, Kapay, Fredy, John Rupture, Mikko, Andal Feye, Sana Ibrahim.

Panel guests are Amadou Ndong, Tshedy, Kapay, Fredy, John Rupture, Mikko, Andal Feye, Sana Ibrahim. This panel focuses on the challenges contributors face in building OSM communities in the South and the strategies they adopt to achieve their goals. It brings together active members of selected OSM communities who will share their experiences and make recommendations for building strong OSM communities. Contrary to the North, the South has many challenges to face since most of them are poor countries where people are in constant search of daily bread whether they are students or young graduates. It is therefore difficult to engage youth and even adults in volunteer work. Those who engage in volunteer work expect to earn a strict minimum of money, such as the cost of fuel to get to the activity site. Those who engage in local community building, however, must have little funds to pay for food and drink, to buy internet connection during mapathon activities or training. Nevertheless, some communities manage to move forward thanks to strategies set up internally. This panel will be an opportunity for us to talk about challenges in different countries and to share the strategies adopted by each side to inspire others. At the end of this panel, participants will be better skilled on how to act for strong and inclusive communities with engaged and empowered members.

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The code samples, status, recent development and roadmap of the open-source community driven project for rendering of (not only) OpenStreetMap vector tiles in a web browser (GL JS) and with native code (Android, iOS, etc).

After Mapbox announced the closure of Mapbox GL JS, their JavaScript library for displaying maps using WebGL, the community around Hacker News gathered on Slack and GitHub and made a collective decision to maintain and further develop the last open-source version of the software and build a 100% free alternative of the project. This is how the MapLibre was born. As a group of individuals, we coordinate the effort and synchronize contributions from multiple teams (MapTiler, Amazon, Facebook, Elastic, Stadia, Microsoft, Jawg, GraphHopper, Toursprung, etc) - working on JavaScript and Native code implementation of the renderers and related ecosystem. Multiple releases have been published, the project has CI checks for contribution, regular steering committee meetings, updated support for TypeScript, several bindings such as ReactJS, the Metal rendering on iOS is implemented (as Apple decided to deprecate OpenGL ES), and many issues and bugs has been fixed. There is plenty of ideas what to do next - from implementation of 3D terrain rendering, to support of non-Mercator map projections, or tighter integration with Leaflet, and much more. Let's explore the current status of the project, learn how to use MapLibre in your own software with practical code samples, and how to join and contribute to the collaborative development and participate on a shared roadmap.

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Pedestrians have been neglected. We’ve seen monumental progress in digital maps, but much of this has been road centric. In this presentation we download data from OpenStreetMap that relates to pedestrians to see how much it differs from the reality on the ground. We contrast different types of cities, seek to understand why pedestrian data is lacking, and look at solutions such as Mapillary that can help make OpenStreetMap more pedestrian friendly.

The evolution of digital maps of the last 20 years has been nothing short of incredible. The experience for the end consumer has continued to improve, with better map data, more intuitive interfaces, and greater portability. A lot of the developments have focused on in-car navigation, with Google Maps, Apple Maps, HERE Maps, and TomTom dedicating significant resources to the space. Even in our beloved OpenStreetMap, vehicle based navigation claims centre stage. In this presentation we’ll explore the state of pedestrian data in OpenStreetMap, how it differs between cities, why it’s important to think about, and how we might collectively improve the quality of pedestrian data. To begin with, we’ll take a look at data downloaded with Overpass Turbo. The data represents nodes, ways, and areas with pedestrian relevant tags such as highway=footway and sidewalk=both. Our analysis focused on five cities with differing characteristics: - Folsom, USA - Heidelberg, Germany - Melbourne, Australia - Stone Town, Tanzania - Yesan, South Korea These cities differ in population, cultural characteristics, urban planning, history, and topography. We’ll explore what kind of OpenStreetMap tags have been used in each city, how close this matches the state of pedestrian infrastructure, and how the cities compare to one another. We’ll then look at some reasons why pedestrian data has been neglected including the limitations of satellite imagery, commercial incentives, and data collection methods. In the final part of the presentation we’ll propose and hopefully discuss some of the tools that could help including Mapillary, StreetComplete, GoMap!!, and Vespucci.

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This panel will bring together a few major corporations active in the OSM Community to talk about their mapping activity and larger involvement within OSM. We will cover the release of datasets, development of mapping tools, and the increasing amount of paid-editing in OSM. Companies include ESRI, Facebook, among others yet to be confirmed. The panelists are Deane Kensock (Esri), Ben Clark (Facebook), Jinal Foflia (Grab), Lukas Martinelli (Mapbox)

The panel discussion will be broken into 3 sections: 1. Introduction (5 minutes): Brief Introduction of Panelists and their involvement in OSM, as quantified in existing work. This includes a very brief overview of paid-editing in OSM and how the scale has increased in recent years, along with the adoption of AI-assisted mapping, and any new datasets made available. This will be positioned within the results of the OSMF 2021 Community Survey and briefly discuss the distinction between corporations as data consumers and corporations as data producers. 2. Formal Panelist Introductions (5-8 minutes each). Each speaker will have a few minutes to describe their company's involvement in and use of OSM. 3. Moderated Discussion (40 - 60 minutes). The audience will have the chance to pose questions in a separate chat / shared document and the moderator will collate questions and ask the panelists. Example questions to the panelists include: - Why OSM? There were likely many possible platforms that your company considered, what was the reason for choosing OSM, and what was this process like? Was there immediate buy-in, or not? - If local volunteer mapping communities are active in the regions you are mapping, how do you communicate and work with them? Are you supporting them directly in any way? - How does your company intend to contribute to OSM? Is it in datasets? technologies? mapping expertise? The list of panel participants has yet to be finalized. The panel will be moderated by Jennings Anderson, an OSM researcher who has written about and continues to research the involvement of corporations in OSM.

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Mappers see the OpenStreetMap Standard layer every time they view OpenStreetMap.org, but who else is using the layer? With usage logs, who is accessing what is broken down for this important OpenStreetMap service.

The OpenStreetMap Standard layer is one of the high profile OpenStreetMap services, but not many people know who uses it, and where. This talk covers the goals of the service, it’s history, how it's designed, and dives deep into how it's used. Using improved logging functionality, we’ll get answers to these questions and more. We’ll cover the policy and technical basics of the service, showing how changes make it to your browser through the various elements of the stack and what we set out to do by running the standard layer, including policy and history. Diving deep into the usage, we’ll look at where people are viewing, where they’re from, daily and weekly usage patterns, and then into what sites and apps people use to view the layer, including the long tail of small sites. You’ll see how different uses put different loads on the OSMF servers, and how load doesn’t come from where people think it is. Many people just want to see their edits on the map, and we’ll cover that too, looking at how they show up and what to do if they don’t. Along the way we’ll learn how to see your changes on the map and see heatmaps, treemaps, and find out just what’s up with the teapots.

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Between December 2020 to March 2021, the OSM Kenya team ran a project whose focus was on growing the OSM community locally: both in terms of membership and diversity. The project was supported through the Facebook and HOTOSM community impact microgrants. In this session, we will share about the community, project: from ideation(community health), motivation to implementation. This will include the activities involved, our experience, challenges that we encountered, and the lessons learned.

Being one of the awardees for the 2020 Community Impact Microgrants by HOTOSM and Facebook, the OpenStreetMap community in Kenya ran a virtual three-month training program for women and girls interested in OSM. While the project was implemented towards the end of 2020, the ideation process began at the start of the year when discussing community health and brainstorming on activities to conduct virtually due to COVID-19 restrictions. This included carrying out a community survey, identifying gaps and challenges, and figuring out what next. Our key focus was community growth, especially in terms of diversity and membership. We had some virtual activities that introduced new mappers to the community but still saw no diversity improvement. We, therefore, designed a women-only training program that would run for eight weeks. We were able to train a group of 35 beginner-level and intermediate-level mappers on all aspects of open mapping. Activities included hands-on workshops, presentations, and mapathons. During this period, we had some successes, learned a lot, but we also had a good share of challenges. We hope that by sharing our experience, other communities interested in implementing similar programs can learn more about the same; we also hope to have discussions that would improve future iterations of the program.

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Learn how to use a variety of R packages to get and visualise OSM data.

As an interactive, open-source, high-level programming language designed for data science, R is a great fit for dealing with OpenStreetMap data and introducing beginners to reproducible scientific computing. In this workshop, you will learn how you can use a variety of R packages that are ideal to: * acquire relevant OSM data in a vector format * clean and process the data * display it in static and interactive visualisations * share a visualisation with others * export the data to other programs for further editing Most data acquisition, cleaning, processing, visualising and exporting can be done writing an R script. For further refinement of an output, other point-and-click programs (like QGIS) might be required, but storing the bulk of the process in a script allows us to easily reapply the same process on an updated dataset, share the process with others so they can learn from it, and provide supporting evidence when publishing results. Basic knowledge of R is preferred to follow along this workshop. If you want to run the commands on your own computer and learn by doing, please install both [R](https://cran.r-project.org/) and [RStudio](https://www.rstudio.com/products/rstudio/download/#download) prior to attending, as well as the necessary libraries for spatial data processing (see [OS-specific instructions](https://r-spatial.github.io/sf/#installing)).

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NLMaps Web is a web interface for querying OSM with natural language questions such as “Show me where I can find drinking water within 500m of the Louvre in Paris”. They are first parsed into a custom query language, which is then used to retrieve the answer by queries to Nominatim and Overpass.

Nominatim and Overpass are powerful ways of querying OSM, but the Overpass Query Language is somewhat impractical for quick queries for unfamiliar users. In order to query OSM using natural language (NL) queries such as “Show me where I can find drinking water within 500m of the Louvre in Paris”, Lawrence and Riezler [1] created the first NLMaps dataset mapping NL queries to a custom machine-readable language (MRL), which can then be used to retrieve the answer from OSM via a combination of queries to Nominatim and Overpass. They extended their dataset in a subsequent work by auto-generating synthetic queries from a table mapping NL terms to OSM tags – calling the combined dataset NLMaps v2. [2] The proposed purpose of these datasets is training a parser that can parse NL queries into their MRL representation, as done in [2-5]. The main aim of my Master’s thesis was building a web-based NLMaps interface that can be used to issue queries and to view the result. In addition, the web interface should enable the user to give feedback on the returned, either by simply marking the parser-produced MRL query as correct or incorrect, or by explicitly correcting it with the help of a web form. This feedback should be directly used to improve the parser by training it in an asynchronous online learning procedure. After observing that parsers trained on NLMaps v2 perform poorly on new queries, an investigation into the causes for this revealed several shortcomings in NLMaps v2, mainly: (1) Train and test split are extremely similar limiting the informativeness of evaluating on the test split. (2) Various inconsistencies exist mapping from NL terms to OSM tags (e.g. “forest” sometimes mapping to natural=wood, sometimes to landuse=forest). (3) The NL queries’ linguistic diversity is limited since most of them were generated with a very simple templating procedure, which leads to parsers trained on the data not being very robust to new wordings of a query. (4) In a similar vein, there is only a small amount of different area names in NLMaps v2 with the names “Paris”, “Heidelberg” and “Edinburgh” being so dominant that parsers are biased towards producing them. (5) Some generated NL queries are worded very unnaturally making them counter-productive learning examples. (6) Usage of OSM tags is sometimes incorrect, which affects the usefulness of produced parses. The detailed analysis is used to eliminate some of the shortcomings – such as incorrect tag usage – from NLMaps v2. Additionally, a new approach of auto-generating NL-MRL pairs with probabilistic templates is used to create a dataset of synthetic queries that features a significantly higher linguistic diversity and a large set of different area names. The combination of the improved NLMaps v2 and the new synthetic queries is called NLMaps v3. A character-based GRU encoder-decoder model with attention [6] is used for parsing NL queries into MRL queries using the configuration that performed best in previous work [5]. This model is trained on NLMaps v3 and used as the parser in the newly developed web interface. Mainly through advertising on the OSM talk list and the OSM subreddit, 12 annotators are hired from all over the world to use the web interface to issue new NL queries and to correct the parser-produced MRL query if it is incorrect. They are assisted by completing a tutorial before the annotation job and by help compiled from taginfo [7], TagFinder [8] and custom suggestions for difficult tag combinations. The collected dataset contains 3773 NL-MRL pairs and is called NLMaps v4. With the help of NLMaps v4, an informative evaluation can be performed revealing that a parser trained on NLMaps v2 parses achieve an exact match accuracy of 5.2 % on the MRL queries of the test split of NLMaps v4 while a parser trained on NLMaps v3 performs significantly better with 28.9 %. Pre-training on NLMaps v3 and fine-tuning on NLMaps v4 achieves an accuracy of 58.8 %. Since the thesis’s goal is an online learning system – i.e. a system that updates the parser directly after receiving feedback in the form of an NL-MRL pair –, various online learning simulations are conducted in order to find the best setup. In all cases, the parser is pre-trained on NLMaps v3 and then receives the NL-MRL pairs in NLMaps v4 one by one, updating the model after each step. The most simple variant of the experiment uses only the one NL-MRL pair for the update, another variant adds NL-MRL pairs from NLMaps v3 to the minibatch and a third variant additionally adds further “memorized” NL-MRL pairs from previously given feedback to the minibatch. The main findings of the simulation are that all variants improve performance on NLMaps v4 with respect to the pre-trained parser, but with some of them the performance on NLMaps v3 degrades. The simple variant that updates only on the one NL-MRL pair is paricularly unstable, while adding NLMaps v3 instances stabilizes the performance on NLMaps v3 and improves the performance on NLMaps v4. Adding the instances from memorized feedback further improves the performance to an accuracy of 53.0 %, which is still lower than the offline batch learning fine-tuning mentioned in the previous paragraph. In conclusion, the thesis improves the existing NLMaps dataset and contributes two new datasets – one of which is especially valuable since it consists of real user queries – laying the groundwork necessary for further enhancing NLMaps parsers. The current parser – achieving an accuracy of 58.8 % – can be used by OSM users via the new web interface currently available at https://nlmaps.gorgor.de/ for issuing queries and also for correcting incorrect ones. Future work will concentrate on improving the web interface’s UX and enhancing the parser’s performance in terms of speed and accuracy.

talk on conference website

Machine Learning is incredibly popular at this time among researchers working with OSM data and on OSM-related problems. But what impact has this work on ML had on the OSM database or OSM community? We investigate the impact on OSM, if any, the ML work within the academic research community has had over the last few years.

# What has machine learning ever done for us? Peter Mooney and Edgar Galvan,
Department of Computer Science,
Maynooth University, Maynooth.
Co. Kildare. Ireland.
peter.mooney@mu.ie; edgar.galvan@mu.ie
### Introduction and background Recently, machine learning (ML) and artificial intelligence (AI) based approaches are being applied frequently to many different types of problems in OpenStreetMap (OSM). Indeed, ML and AI have being used extensively by the research community for a plethora of applications and problems both related and unrelated to OSM. Wagstaff (2012)[1] suggests ML offers "a cornucopia of useful ways to approach problems which defy manual solutions". In specific relation to the geospatial domain, ML approaches have been reported at least as early as a decade ago with work by authors such as Werder et al. (2010)[2] on interpretation of buildings in settlements and detecting road intersections from GPS traces by Fathi and Krumm (2010)[3]. Around this time, interest in the combination of ML and OSM began to emerge. Funke et al. (2015)[4] argued that many aspects of OSM data might be suitable for "extrapolation or classification using ML". Many examples have emerged with ML approaches being used to consider problems such as: predicting or recommending tagging for objects, object classification based on contextual or proximity information, tag usage checking, automated mapping approaches, to mention some problems. Jennings et al. (2019)[5] showed that Facebook’s recent mapping campaign in OSM used ML to detect road networks from satellite imagery which are then validated by OSM editors and the local OSM communities. Examples also exist where OSM is used in ML approaches for other geospatial classification problems (Wu et al. (2020)[6], Jacobs and Mitchell (2020)[7]) while authors such as Feldmeyer et al. (2020)[8] used machine and deep learning algorithms with OSM for developing socio-economic indicators. Audebert et al. (2017) provided additional examples and argued that OSM's richness means it can be used in difficult problems such as semantic labeling of aerial and satellite images. In addition to the observations by Vargas-Munoz et al. (2021) in their recent review of ML approaches in OSM, we can usually observe ML and OSM interaction in one of three ways: (1) ML approaches are used to improve or correct OSM data, (2) instances where OSM is used as a means of training ML models for some specific task such as building segmentation, road speed estimation (Keller et al, 2020 [10]) or land use classification (Schultz et al. 2017 [9]) or (3) where the contribution patterns of OSM contributors are analysed using ML techniques as in work such as that by Jacobs and Mitchell (2020)[7]. In this submission we ask the following question. With all of the many applications and integration of ML and AI with OSM, over the past number of years, how many of these applications and approaches have been adopted or used by the OSM community? Furthermore, what are the benefits or impact of these efforts from the research community with ML and AI approaches to the OSM project and OSM community? We believe that there is significant scope for ML researchers to make impactful and helful contributions directly within OSM on problems such as tag updating and correction, added intelligence within OSM editing software, intrinsic quality analysis, etc. ### Methodology and Findings achieved A systematic review of approximately 60 peer-reviewed academic journal and conference papers will be reported. These papers are selected on the following basis that the paper(s): (1) clearly outlines an ML or AI approach using OSM data, (2) tackle a problem known in the OSM community such as tag prediction, contribution patterns, or geometry correction. Paper metadata such as title, keywords, and abstract contents are used to select the papers. Manual checking of the papers is also undertaken to ensure that the content of each paper relates to our selection criteria. A classification of these papers will be developed based on the following set of questions: * What are the most common ML approaches used by researchers for the three instances outlined above? For example, Learning Problems (supervised, self-supervised, reinforcement), Statistical Inference (Inductive, Deductive), etc. * What are the most common types of problems in OSM tackled by ML approaches? For example, automated tagging, contribution pattern analysis, intrinsic quality analysis, object classification, etc. * Are the approaches reproducible and replicable for other regions or areas within OSM? For example, is a particular ML approach limited to a specific geographical area or thematic area (such as roads, buildings, waterways, etc.) in OSM. On this classification we then report a narrative on our findings on the benefits and impacts of these efforts to the OSM project and the OSM community. We are working on this analysis at the time of writing. ### Final Discussion of scientific contributions As suggested by Jacobs and Mitchell (2020)[7], ML can "contribute to the diversification and quality of available assessment methods for OSM" while Feldmeyer et al. (2020)[8] argues that the application of ML to OSM can reveal the "untapped potential for knowledge generation" in OSM. In our work, we argue that we must not get carried away with the combination of ML and OSM purely for the sake of it. OSM, as a massive open geospatial database, is a very attractive source of (geo-)data for researchers and practitioners looking to train, benchmark and test ML approaches. Consequently, we can confidently state that, after well over a decade of reported results in this domain, researchers have produced many excellent research and knowledge outputs using the ML and OSM combination. Now we enter a phase of technological and scientific development with ML and OSM where we must ask how can all of this ML knowledge contribute effectively to the OSM database and OSM community. Grinberger et al. (2019)[11] argue that while efforts to establish and strengthen interaction between the research community interested working with or in OSM and the OSM community itself have generally been positive. However, opportunities exist to enhance interactions between these two communities and perhaps ML could be the catalyst for a new interaction. Based on this the scientific contribution of this work is multi-faceted. Firstly, this paper will stimulate debate about the contribution of these ML approaches to the improvement of OSM data and enhancement of the OSM community. Secondly, this work will highlight situations where these ML approaches have delivered genuinely new and novel outputs of interest to OSM in general. Finally, this work will issue the challenge to the academic community to apply ML to several interesting and open problems which are of mutual interest to both the academic and OSM community.

talk on conference website

OpenStreetMap (OSM) constitutes a new open geographic database and offers several possibilities of adding local knowledge. While the importance of local knowledge is largely acknowledged in the OSM community, relatively few scientific studies have evaluated them. This study presents a framework to measure local data contribution in OSM in three case studies. The results highlight a framework for measuring local data in OSM as well as the distinct mapping stories of local OSM communities.

OpenStreetMap (OSM) has proven to be a valuable source of spatial data for many applications, including humanitarian aid. Information on buildings and roads - that can be provided by remote mapping - is of highest concern for many humanitarian applications. However, further information - that can only be mapped on the ground -  is of high importance for finer scale humanitarian action. Road surface information, type of material and information on the use of a building (health site, school,...) is highly relevant.  OSM offers several possibilities of adding local knowledge [1]. Recent works deals with analyzing and classifying data production in OSM [2] and intrinsic analysis has gained popularity as an indicator for measuring quality of OSM data [3–6]. Nevertheless, relatively few scientific studies have touched on "local knowledge" and local data in OSM in sufficient detail. The question of how much local knowledge is added and what kind of local data is added remains unanswered. Addressing this question is important since only local knowledge provides access to the plethora of contextual information that is necessary for many purposes. The term "local knowledge" is  often debated in the OSM community due to its ambiguity. Consequently, it is hardly taken into account by researchers when evaluating OSM [1]. This study presents a metric to measure local data contributions in OSM and analyzes temporal patterns of local contributions at three case studies. The aim of the metric is to identify archetypes of places representing a variety of contextual information. Firstly, we evaluated Rebacca Firth's framework on OSM contribution types that focused on the humanitarian context (see Twitter post: https://t.co/rDaSraiVZF). Secondly, we discussed with local community working groups how to measure local data contributions ("What exactly are local OSM data to you?"). The outcome of the community discussion provided valuable information to design a generalized workflow for measuring local data contribution in OSM. Subsequently, we identified aspects on which the local communities agreed with respect to perception of local data. Based on this first insights, we developed a classification schema for measuring local data in OSM that is "fit-for-purpose" for local OSM communities. This schema consists of four main levels and assigned OSM tags that could be used as indicators for each level. Thirdly, we explored the temporal evolution of local data in OSM for three unique regions. These regions mapping activities are influenced by local mapping organizations. (i) Ramani Huria in Dar es Salaam, Tanzania, focusing on flood resilience (ii) Crowd2MAP mainly operating in the Mara region, Tanzania and focusing on identifying features that can support the fight against girls and women at risk of female genital mutilation, and (iii) Power mapping project by Youth Mappers in the Koindugu, Sierra Leone, focusing on mapping electrical grid infrastructure. We used the ohsome API to access the full history of OSM. We determined the density and the ratio (as the sum of all OSM tags to the number of OSM elements) per month for each region and localness level.  The outcome of the community discussion showed that local mappers/editors had different perceptions about local knowledge. The type of local data produced depends on: (1) the context within which the data is produced and (2) the character/interest of the individual mapping them. However, the local data produced could be broadly categorized as "core" or "specific". The "core" category consisted of the objects that cut across almost all projects or activities (e.g., buildings, roads, place names and administrative boundary) and the category "specific" were special elements mapped as a results of a particular interest or aim of the project (e.g., culvert, drains, access types, parking type).  To develop a metric for local data analysis, we classified OSM data into four main levels based -  level one consists of objects that can be derived easily by remote mapping from satellite images such as roads and building (this is information that does not require local knowledge), level 2 focuses on place names and administrative boundaries which are frequently imported, level 3 focuses on the presence of general (e.g., residential and commercial) or specific amenities (e.g., school, clinic, and point of interest) and level 4 focuses on micro-data that provides further contextual information about an object (e.g., road:maxspeed, surface condition). Level 1 and 2 mainly fall into the "core" category whereas level 3 and 4 mainly belong to the "specific" category (which will vary across different regions).  Our results show that the amount of features in OSM decreased from level 1 to level 4. The ratio between level 1 and level 4 could be used as an indicator for how widely local information is present in OSM at a specific location. Thereby, it can provide insights on the quality of the OSM data and fitness-for-purpose for applications that need information beyond the existence of highways or buildings. From the temporal analysis, we observed that the amount of features in OSM decreased from level 1 to level 4. The ratio between level 1 and level 4 shows how widely local information is present in OSM at a specific location. Thereby, it provides insight on the quality of the OSM data and fitness-for-purpose for applications that need information beyond the existence of highways or buildings. Most of the mapping in the selected region started in 2015. By digging deeper into the objected mapped, each selected region depicts unique characteristics which is largely shaped by the interest of contributors/organizations.  Mapping patterns are clearly distinct from each region with respect to the development of tags. For example, there was high local data regarding waterways, drainage, and solid waste in Dar es-Salaam and very low in Mara region and Koindugu. It reveals the distinct mapping stories of individual or organization. Our results show further, that there is no common path from level 1 to  Level 2 to level 3 among the different regions. For the case of Dar es Salaam, mapping of features of the three levels has happened more or less simultaneously. Mapping in the Mara region focused first on place names (level 2) and then on amenities (level 3) as well as buildings and roads (level 1). For Koindugu, mapping of level 2 started in 2011 already and was followed by mapping of buildings and roads (level 1) in 2014 and amenities (level 3) from 2017 on. The classification schema helps to conceptualize a metric to measure localness of OSM data at different levels of details. This metric can be easily used to group OSM data into the categories "core" and "specific". By analyzing the temporal patterns, we identified that contribution of local data was highly unequal and largely depended on the interest of the mapper(s). The research shed light on the richness of contextual information in OSM as well as an indication for the quality of data. In future research we would like to extend the results presented here by including more regions and more perspectives from local OSM communities. By doing so we hope to be able to extend the definition of local data by considering the editors' local knowledge as well.

talk on conference website

Everyone knows the "standard" map style on openstreetmap.org and most also know at least a bit how to read it. But hardly anyone is familiar with everything the map shows. Based on examples from the map submitted by community members this session is going to explain the design of the map and what those things you can see in it mean.

This is going to be an interactive session where participants can [submit case examples](https://wiki.openstreetmap.org/wiki/User:Imagico/Understanding_the_map_SotM2021) (in the form of links to specific examples on the map - [on the wiki](https://wiki.openstreetmap.org/wiki/User:Imagico/Understanding_the_map_SotM2021) or [on a pad](https://pads.ccc.de/Understanding-the-map-SotM2021)) to be discussed. The focus of the discussion will be on explaining the design of the map and what cartographic and technical considerations stand behind it. The aim is for participants - both newcomers and experienced mappers - to better learn how to read the map, to better understand the cartographic and social challenges of designing a rich, global, real time updated map based on OpenStreetMap data for a truly diverse international and multi-cultural audience and hopefully to get some participants more interested in community map design and to encourage them to either contribute to OSM-Carto or to contribute to or start their own regional or thematic map design projects. Secondary goal of the session is for OSM-Carto developers to better understand where the map is difficult to read intuitively for the OSM community. Links for further background information: * [https://wiki.openstreetmap.org/wiki/Standard_tile_layer](https://wiki.openstreetmap.org/wiki/Standard_tile_layer) * [https://wiki.openstreetmap.org/wiki/Standard_tile_layer/Key](https://wiki.openstreetmap.org/wiki/Standard_tile_layer/Key) * [https://wiki.openstreetmap.org/wiki/Featured_tile_layers/Guidelines_for_new_tile_layers](https://wiki.openstreetmap.org/wiki/Featured_tile_layers/Guidelines_for_new_tile_layers) * [https://github.com/gravitystorm/openstreetmap-carto/](https://github.com/gravitystorm/openstreetmap-carto/) * [https://github.com/gravitystorm/openstreetmap-carto/blob/master/CARTOGRAPHY.md](https://github.com/gravitystorm/openstreetmap-carto/blob/master/CARTOGRAPHY.md)

talk on conference website

We look at interactions between Corporate and Non-Corporate Editors as reflected through co-editing patterns in the OSM data. We use Social Network Analysis on 12 networks generated from four different locations and 3 different timepoints and our results show the vibrant co-production of OSM data generation. There are interactions between all editors but Corporate Editors tend to interact at a higher rate with each other. The seniority of editors and the interactions also differ between Corporate and Non-Corporate Editors.

OpenStreetMap (OSM) data is produced by a vibrant online community of mappers. To be more specific, OSM data produsers represent a plethora of individuals with different motivations, methods of data contribution, and usage (Budhathoki & Haythornthwaite, 2013; Coleman et al., 2009). Thus, OSM contributors have been aptly described as a community of communities (Solis, 2017). In recent years, corporate editing teams have introduced a new dynamic in the discussion on communities in OSM; editing teams hired by corporations, such as, Apple, Facebook, Microsoft, Uber, are capable of contributing thousands of changesets a day (Anderson et al., 2019; Anderson & Sarkar, 2020). Additionally, corporate editors (CEs) tend to focus their editing on particular types of map features. These two attributes of corporate editing can lead to CEs breaking off into a siloed group of their own with little or no interaction with the rest of the editors on the map. Previous research on the OSM community using similar methods showed there was limited collaboration between editors with most objects being edited only a few times (Mooney & Corcoran, 2012). Senior editors in particular perform a majority of the mapping work on their own, but do interact with others through co-editing (Mooney & Corcoran, 2014). Since these studies were performed, the OSM community has grown significantly and the community dynamics have also evolved with more individual and organized participation (e.g. CE). Here, we use a data driven approach to characterize the interactions between the CEs and the rest of the OSM community. We define interactions through editing patterns. That is, we construct a network of interactions where each node represents an editor, and two nodes are connected if they have edited the same map object. If the mapper of node A edits an object last edited by the mapper of node B, then an edge connecting these nodes exists and is directed from A to B. We utilized the OSM-Interactions tilesets to construct these networks (Anderson, 2020). These vector tiles contain the editing history of all highway and building objects at zoom level 14. They include minor changes to the geometry of objects in which only nodes are moved, but the parent way is left untouched. In this way, we are capturing the complete history of map objects in OSM, as opposed to just changes to the basic OSM elements (primarily nodes or ways). In keeping with the objects which are primarily edited by CEs, we focused only on highway and building objects for construction of the network. The nodes are further annotated with a binary category representing whether they are a CE or not. We classify a mapper as being a CE or not by comparing usernames in the network to the disclosed lists of usernames on a corporation’s OSM wiki or Github page. We focus on 4 locations: Egypt, Jamaica, Thailand, and Singapore. We create networks for each of these locations at 3 timepoints, 2015, 2017, and 2019 to characterize the changes between over time. Thus, we constructed and analyzed 12 networks. The locations were chosen as they all have different groups of CEs active. Across all networks, the Largest Connected Component (LC) accounted for 93.6% of all nodes highlighting significant interactions amongst all mappers. Within the LC, the rate of growth of CE nodes exceeds the rate of growth of non-CE nodes at rate of 11:1 between 2015 and 2019. However, both types of editors (CE and non-CE) have a comparable number of in and out degrees in each place, indicating that they edit other people’s work and have their work edited at a similar rate. In terms of who edits whose work, CE’s edit other CE’s work most often, but interaction between CEs and non-CEs have also grown through time, keeping the network connected. With regards to age of the mappers (calculated in terms of their enrollment date in OSM) and the volume of edits they perform, younger mappers in both groups tend to edit others' work at a higher rate than senior mappers, but there is more variation in these statistics for non-CE mappers. This is a finding contrary to previous research on editing interaction patterns mentioned above. Additionally, characterizing the time between edits show that edits made by CE’s persist for a slightly shorter duration than edits made by non-CE, primarily due to other CEs editing the same object soon after. In conclusion, the editing networks highlight the vibrancy of data co-production. The volunteer editor and CEs are interacting with each other's edits to produce the map. The per-group interaction is nuanced and shows unique editing patterns which warrant further investigation. During the timespan of this study, the rate of growth of the CE community was faster than the non-CE community, but whether the pattern will hold over time and whether other locations exhibit the same pattern require more research.

talk on conference website

This talk presents results of an experiment conducted on the temporal accuracy of OpenStreetMap, and provides insights into the temporal dynamics with which changes in real-life appear in OSM.

The ability to provide timely information compared to traditional collection methods of geographic information is generally considered as one of the main advantages of volunteered geographic information (VGI) since its emergence in the 2000s (Goodchild, 2007). In addition to several anecdotal examples illustrating how VGI data can provide more up-to-date information than authoritative sources, the literature provides ample evidence on the usefulness of VGI in applications that require timely geodata, such as disaster management (Horita et al., 2013; Neis & Zielstra, 2014). For example, the Haiti earthquake relief effort in 2010 laid the foundations of how remote contributors of OpenStreetMap (OSM) and other platforms can make a difference and aid responding humanitarian agencies after a crisis (Zook et al., 2010). The Humanitarian OpenStreetMap Team has made numerous contributions and helped save lives at numerous instances ever since (Herfort et al., 2021). However, apart from these examples, the temporal dimension of VGI has not received much research attention outside the application of disaster management, and there is a huge gap between assessing temporal accuracy and other factors of data quality, such as spatial accuracy (Antoniou & Skopeliti, 2015; Yan et al., 2020). Aubrecht et al. (2017) highlighted the lack of formal acknowledgment of temporal aspects in the concept of VGI and proposed a framework called ‘Volunteered Geo-Dynamic Information’ to fully integrate spatial and temporal aspects of VGI. Other works utilizing the temporal component in VGI often focus on the behavior of contributors rather than the currency and temporal validity of map features they contributed (Bégin et al., 2018; Haklay et al., 2010; Neis & Zipf, 2012), or studied the evolution of data over time (Girres & Touya, 2010; Zielstra & Hochmair, 2011). While these approaches are useful, by nature they cannot provide a quantitative measure of how current OSM (or VGI in general) is. Arsanjani et al. (2013) noted during their investigations that the temporal accuracy of OSM could not be measured using their traditional extrinsic method, because OSM data was compared to authoritative data that did not contain temporal information (i.e. most recent street configuration regardless of when road segments were built or renovated). Another project, ‘Is OSM up-to-date?’ recognizes the lack of information on temporal accuracy and developed a tool that uses an intrinsic approach to visually show features that potentially contain outdated information (Minghini & Frassinelli, 2019). However, by nature, an intrinsic approach can also not provide an absolute measure of how up-to-date OpenStreetMap is. This research attempts to fill a gap in the literature by conducting an experiment on the currency of VGI. Using OSM data as a case study, it will measure the temporal accuracy of selected map features. This research overcomes previous limitations by using official data provided by the Florida Department of Transportation (FDOT). The dataset contains details about state-funded highway construction projects, including the date these projects were completed, therefore accurately measuring the temporal accuracy of OSM features is possible by comparing dates projects were finished with the time at which corresponding OSM edits in the database were made. This time difference describes how long it took for the OSM community to adapt to real-world changes and update the map database accordingly. The historical version of highway construction projects was filtered to projects completed between May 15, 2016 and April 1, 2021. Further, only a subset of projects were used, that resulted in either 1) new infrastructure (new roadways, roundabouts or highway ramps), 2) new lanes in existing roadways (excluding bike lanes), and 3) new bike lanes or paths. Other construction projects, such as traffic improvements, road resurfacing, regular maintenance (e.g. bridge rehabilitation), etc. were excluded, since a useful, high-quality road network database can be maintained without the addition of these information, therefore, they are less likely to migrate into OSM. The methodology uses augmented diffs from the Overpass API to find all changes that occurred on OSM highway features (creation, modification and deletion) and are spatially and temporally close to construction projects. These changes are then matched with a record from the highway construction dataset. Irrelevant changes (i.e. changes made to other highway features) are removed. This is done by manually interpreting and evaluating changes and construction projects using a description field (e.g. “SR 61 WAKULLA SPRINGS RD @ CR 2204 OAK RIDGE ROAD INTERSECTION - ROUNDABOUT”). The data extraction algorithm initially queries the Overpass API for changes one week beyond the completion date of a particular project. In case no relevant change can be found, iterative queries for 7-day-long time slices are made until a relevant change is found, or until the current date is reached. Lastly, the time difference between the end date of construction projects and the first OSM change that introduced the change in OSM are calculated. For example, the description field above mentioning State Road 61 (SR61) can be found with the following Overpass query (https://overpass-turbo.eu/s/16XV) that uses the location of the highway construction project. Interpreting whether an extracted change is relevant or not can also be verified using changeset comments: (https://www.openstreetmap.org/changeset/87938707). In this example, the changeset comment “Added new round about.” confirms that the OSM edit is related to the FDOT dataset. Comparing the construction end date (July 3, 2019) and the time when this change appeared in OSM (July 13, 2020) yields 1 year and 10 days, which is the time it took the OSM community to adapt a real-world change and bring the database up-to-date. This talk will be structured as follows. First, results of a comprehensive literature review on the temporal aspect of OSM research will be given to highlight the lack of data-driven, quantitative research on the temporal component of OSM and VGI. Then, using the filtered FDOT construction dataset that contains 23 new highways and roundabouts, 64 new bike lanes and paths, and 129 new traffic lane additions, the results of an exploratory data analysis about the currency of OSM will be presented. The summary and descriptive statistics of a reasonably large sample will provide insights into the currency of OSM and the dynamics of temporal accuracy. Lastly, limitations of the experiment will also be discussed. These include the reference dataset, that does not contain federally or locally funded projects, therefore misses a large number of constructions, and the methodology, that cannot capture the diversity of the OSM community and also disregards changes beyond the transportation infrastructure. This experiment is the first attempt to investigate the timeliness and currency of Volunteered Geographic Information using large sets of data. Future work will conduct analysis using more VGI data sources outside the domain of mapping applications (e.g. Points of Interest in check-in trackers and review applications), new methodology using tile-reduce, OSM QA tiles and vector tiles built from other datasets. The new methodology will be scalable and will allow for analysis across world regions. Furthermore, a rule-based decisions approach based on tags and semantics will be used to eliminate the need for manually checking and verifying whether VGI updates correspond to the reference dataset or not.

talk on conference website

It Consists in a proposal for a QGIS Plugin for Spatio-temporal analysis of OSM data quality in an area of Brazil.

The development of methodologies to evaluate geospatial data quality is one of the most important aspects to be considered while obtaining these data. For the developing countries, such as Brazil, the lack of investment for the maintenance of the topographic mapping, especially on a big scale, is a recurrent challenge to the National Mapping Agencies (ANM) [1]. For example, studies reveal areas in Brazil that have never been mapped and that the topographic mapping in the 1:25.00 scale is nearly 5% of its extension [2]. The technological advances enabled a series of methodologies for obtaining geospatial data [3]. One example is presented as Volunteered Geographic Information (VGI) [4]. In this case, the update of information may occur faster and with a reduced cost in detriment to the traditional structures of topographic mapping [5]. A successful case of VGI is the OpenStreetMap (OSM) platform, which presents the growth in the number of contributors and contributions or mapped features. To comprehend the behaviour of the OSM features and their integration potential to the topographic mapping, different surveys worldwide have put efforts to evaluate its quality, whether by its extrinsic [6, 7] or intrinsic [8] aspects. In this regard, some studies have evaluated the quality of OSM's features by combining extrinsic and intrinsic aspects, like [9], that evaluated the positional precision of OSM based on the combination of the edition's history. Besides that, the most recent researches have focused on comprehending spatial and temporal aspects of events in OSM contributions [10], as well developing add-ons for evaluating data quality, as is presented by [11], that developed a QGIS toolbox to evaluate parameters of the intrinsic quality of OSM features. The literature identifies as one of the main challenges for the integration processes, the heterogeneity of the data. Once the quality may vary according to the study area, the indicator used of even the spatial variations through time in the same region. In this context, to understand the adjustment of OSM's resources to the topographic mapping, it is crucial to connect aspects related to the quality and heterogeneity of data. Researches like [1] argue that, based on the obtained quality, the resources resulting from VGI may be used to integrate, detect changes or report errors. Therefore, classifying resources from OSM according to their usability in a certain region becomes essential, especially in developing countries like Brazil. Besides that, the research that explores issues of quality, heterogeneity, and contributions patterns of OSM is still not widespread in developing countries [12]. Given the importance to classify OSM features according to their usability for a given region, especially in developing countries, few researchers explore quality, heterogeneity, and contribution pattern issues of OSM in Brazil. we proposed a hypothesis that understanding aspects of the extrinsic and intrinsic quality of the quality of OSM features, related to spatiotemporal aspects of contributions in developing countries, will help decision making regarding the influence of the dynamics of insertion of features concerning quality. Thus, this research has as an objective to evaluate the extrinsic quality of OSM features for the county of Salvador-Bahia-Brazil (the northeast region of the country). Therefore, we investigated indicators of positional accuracy, thematic accuracy and completeness, the visualisation of heterogeneity of data, and the analysis of the edition history. To accomplish the evaluation of extrinsic quality, the OSM features were compared to the topographic mapping of the country regarding the Cartographic and Cadastral System of the County of Salvador (SICAD, 2006) and features from the Urban Development Company of the State of Bahia (CONDER). The analysis of positional and thematic accuracy was made through procedures of feature sampling. The analysis of completeness occurred from comparing the total of available features. The verified categories were features from the road system, religious, educational, and health buildings. We divided the municipality of Salvador into sub-regions to identify different local patterns of quality in the analysis of thematic accuracy and completeness. The visualisation allows obtaining the data's heterogeneity through a plugin developed in the software QGIS, making the planimetric positional evaluation for point and line features. The statistical procedures for developing the plugins were realised based on the Brazilian law to evaluate geospatial data quality analysis [13] and based on the method of double buffer proposed by [14]. The plugin is available, and it is possible to be accessed in the online repository https://github.com/eliasnaim/AcuraciaPosicional_PEC-PCD. Even though the final results comprehend aspects of Brazilian law, they can be replicated to obtain the discrepancies and posterior adjustments. We used the OHSOME Application Programming Interface (API) (identify the patterns concerning the OSM editing history. Thus, from the adaptations done in scripts given by researchers linked to OHSOME, it was possible to identify the aspects of OSM contributions between 2008 and 2020. We also tested the generation of regression curves and calculated the number of daily contributions to identify these patterns. These verifications were occasioned through the generation of an evolving rectangle of 5x5 km in the study area. The disposition of the rectangle was given through a visual analysis with a larger quantity of OSM features. The evaluation of extrinsic evaluation highlighted the variability of the results obtained in [15]. In analysing the positional accuracy, the scale found varied from 1:20,000 to 1:30,000, while the discrepancies between the mapped coordinates and the reference one varied between 10.27m and 0.12m. In analysing completeness, the road system presented a percentage of 82%, while in the other features, the variation was from 29% to 46%. When analysing thematic accuracy, it turns out that the primary source of errors is related to the absence of names in editing. In the analysis referent to the history's growth of represented features, it was possible to notice a near-linear function, with an R2 value of 0.94. This aspect gives the initial premise that it is possible to model patterns of contributions and associate them to the saturation level of the quantity of added elements in a particular area. Besides that, it was possible to observe that the patterns of collaboration can be affected by different variables because it was noticed that in 2016, more than 800 features were added in a short period. These aspects can be related to events such as data importation or mapathons. The development of add-ons for evaluating OSM data quality that departs from the making of statistical procedures up to visualising the heterogeneity of data will assist in the decision-making as to data quality. The magnitude of discrepancies did not present patterns and that this may vary according to the period of edition and the database used for the contributions. We noticed the relevance in identifying the aspects of quality and heterogeneity in OSM contributions. For Brazil, identifying these characteristics may numerally indicate the integration potential of these data to the authoritative mapping. Besides that, it will estimate the influence of unusual agents, like it is the case of data import in the contributions. The continuity of the studies is recommended to identify the causes of different patterns of growth and the continuity of studies to automatise the quality procedures.

talk on conference website

Organized mapping and institution / corporate based mapping keeps rising in the OpenStreetMap community. This talk is inspired by my observations whilst performing Quality Assurance as part of my personal OSM contributions in Ghana; highlighting some problems, solutions and recommendations.

Many contributors be it, a hit-and-run, enthusiast or institution are using our ONLY OpenStreetMap data, and the community saves so many things in our daily lives. The quality of data added to OpenStreetMap is of high importance if we have to keep saving lives and all other things. Data quality can not be ignored, this is very much discussed on various mailing lists, channels and whenever we speak about OpenStreetMap, it is likely to be the first question we expect from our audience who had no idea about OSM in the first place. Contributing to OpenStreetMap mostly in Ghana, I have come across a couple of mapping activities that I am sometimes not sure what to do or say and left with only one option; want to know :) ? This presentation will explore some practices in relation to contributing to OpenStreetMap and focuses on Ghana as the Area of Interest. It will demonstrate some of my observations in Ghana: * how they came about and how I will deal with them or have resolved them and; * how most of these can be improved and avoided in the future. At the end of this talk I hope to have convinced you enough to take full responsibility whenever you make a changeset; Quality or Quantity.

talk on conference website

This track gathers various lightning talks of 5 minutes each.

## Being ohsome with R *Oliver Fritz* | *[ofr1tz](https://www.openstreetmap.org/user/ofr1tz)* How to query the ohsome API from R to aggregate and extract elements from the OSM history. ## Introducing tilemaker 2.0 *Richard Fairhurst* | *[Richard](https://www.openstreetmap.org/user/Richard)* Vector tiles are great! Having to maintain a rendering database isn't. That's where tilemaker comes in. SOTM 2021 will see the release of the [all-new v2.0](https://github.com/systemed/tilemaker) which makes vector tiles speedily, on your own hardware, without a database. Richard Fairhurst explains how it works and how you can use it. ## GeOsm: The first mapping data-based social network to connect and empower communities. *SOB Willy Franck* Our goal is to create a mapping data-based social network for territory stakeholders. We want to work with different actors of the digital ecosystem to facilitate the promotion of our tool to those who need it. And it will be up to each community to appropriate the approach in an innovative way to help states, associations, people, companies and the environment. ## State of The Map Africa 2021 *Sharon Omoja* | *[shazomojah](https://www.openstreetmap.org/user/shazomojah)* The State of the Map Africa conference celebrates the culture of open mapping, open data and GIS and its impact across Africa. In this talk we will give updates and plans for this year's State of the Map Africa conference. ## Presenting New Numbers: Quantifying the Increase in Paid Editing Since 2018 *Jennings Anderson* There are now more than 2,500 active _paid editors_ in the OSM Community. The last time we comprehensively quantified this editing activity involved just 1,000 editors across ten companies. This talk describes the increase in paid editing since 2018 with country-level breakdowns of editing activity and information regarding new editing teams and the growth of the existing teams. ## Tanga Buildings Import and Community Mapping *Antidius Kawamala* | *[KAWAMALA](https://www.openstreetmap.org/user/KAWAMALA)* This is an Initiative for scaling up the OpenStreetMap Project by addition of buildings footprint data for entire region of Tanga,Datasets was developed by Ardhi University Students (ARU) in Collaboration with Tanzania Communication Regulatory Authority (TCRA) in 2021. Furthermore, There are different community mapping activities as micro-works going on in the region, as the mission of enriching and making freely global spatial data as well as capacity building to the local community on how to use free and open source tools in contributing data to OSM. ## Activities performed by IRDP YouthMappers on 2021 *Shabani Magawila* | *[SHABANI MAGAWILA ](https://www.openstreetmap.org/user/SHABANI MAGAWILA)* [IRDP YouthMappers](https://twitter.com/irdp_mappers) is one of the Active Chapter available in Dodoma, under the supervision of the Institute of rural Development Planning - Dodoma. The chapter has been able to participate in various projects and was able to organize several trainings. With the skills we had we were able to help our fellow students to complete their Field Work Practice.

talk on conference website

We develop and test user embeddings approaches to vandalism detection in OSM. We successfully demonstrate improvements to previous vandalism detection methods, and additionally how the user embeddings can further be applied to detect different communities of mappers. We validated the embedding model with a prepared vandalism corpus that we are also releasing to the OSM community.

With more than 11B edits from 1.6M unique mappers and openly editable by anyone, the OpenStreetMap (OSM) database inevitably contains vandalism. Our approach to detecting it leverages the analytical power and scalability of machine learning through OSM user embeddings. Embeddings are effective in capturing semantic entity similarities that are not explicitly represented by the data. Since word embeddings were first introduced based on the assumption that words adjacent to each other share similar meanings [1,2], the concept of embeddings has been extended beyond word representations to any entity, so long as one can produce a meaningful sequence of the entities. Therefore, we build OSM user embeddings with mappers as entities by constructing sequences of mappers based on shared editing histories and similar behaviors. **Methods** _Creating a Vandalism Corpus_ Development of automated vandalism detection methods in OSM has been slow in part because there is no published corpus of bad or vandalized edits from which to train and validate [3]. Vandalized name attributes are especially problematic because this text is rendered on the basemap. The most infamous instance of this type of vandalism was the changing of "New York City" to an ethnic slur; this name attribute was subsequently rendered on maps drawing from OSM data [4]. As part of this work, we construct and make available the first OSM vandalism corpus for the name attribute of OSM features. Potential examples of vandalism are collected from the OSM Changeset Analyzer (OSMCha) web-based validation tool. These records are then manually reviewed by the Facebook mapping team to identify egregious name changes. Negative samples (non-vandalism) were randomly sampled from a previously validated vandalism-free snapshot of OSM. All of our examples are extracted from OSM data only, no external or conflated data sources. _User Embeddings_ To construct meaningful sequences of OSM users where adjacent users share similar mapping patterns, we analyzed the edit history of every OSM object and the temporal/semantic editing patterns of individual mappers. These sequences were then fed into a word2vec skip-gram model to train OSM user embeddings. **Shared object editing histories** are sequences of OSM users who have edited the same object, in chronological order of editing. These sequences represent mappers who share interest in the same objects on the map. This yields 2B sequences of mappers. **Semantic and temporal mapping patterns** are sequences of OSM users that have shared editing characteristics with regard to how and when they edit the map. Starting with _changesets_, we extract the following keys for each OSM element edited in a given changeset when present: `addr:country`, `admin_level`, `amenity`, `building`, `highway`, `natural`, `place`, `source`. Additionally, we extract the following metadata: the presence of `name` tag, the `version` number, the editing software (e.g. iD editor, JOSM), and any hashtags (possibly denoting specific mapping campaigns). Finally, we group all of these edits by two types of temporal patterns: first, the date of the changeset, and second, the hour of the week of the changeset, per year (with 168 hours in a week, we aggregate across each _week-hour_ in a given year). This yields 30M sequences of mappers. **Results** _Community Detection_ OSM is comprised of many distinct groups of mappers; considering each of these groups a different sub-community makes OSM a "community of communities" [5]. The creation of the temporal and semantic editing patterns was specifically designed to create sequences of mappers with high likelihood of belonging to the same community. One type of easy-to-identify communities are corporate editing teams: groups of employees that are paid to edit OSM [6]. Results of corporate editing team detection can be easily validated against published lists of known editors. The five largest corporate mapping teams are Apple (>1,200 mappers), Amazon (>700), Grab (>550), Facebook (>250), and Kaart (>200). These counts are based on extracting affiliation from a mapper’s OSM user profile, looking for sentences such as “I work for Amazon" and are likely an under-representation [7]. To validate the performance of the model’s ability to successfully identify members of an editing team based on editing semantics, we used the cosine similarity to compare users. First, we identified the 100 most similar users to the _top 10 most active mappers_ in each company (by number of changesets). Next, we confirm how many of the top 100 most similar users are also on that team. This is a measure of recall for our model. Amazon is the most identifiable team, with all 100 of the most similar editors also belonging to the Amazon Logistics data team. The mean cosine similarity (`mcs`) among these 100 mappers is 0.98. Apple is the second most identifiable with 97% of the top 100 most similar mappers also belonging to the Apple data team and an `mcs` between the top 10 and these 97 users of 0.94. Third was Kaart, with 96% and `mcs=0.88`. Facebook was fourth with 87% and `mcs=0.87`. The Grab data team, however, was more difficult to identify: only 68% of the top 100 most similar mappers were also part of the Grab data team. The `mcs` between these 68 mappers and the top 10, however, is high at 0.94. _Vandalism Detection_ To detect vandalism, we train a Gradient Boosting Decision Tree (GBDT) model, which consists of metadata, user reputation, object history, and content features. We applied OSM user embeddings into this model by creating two embedding features, `kmeans_cluster` and `cos_sim_last_5_users`. To create `kmeans_cluster`, we ran k-means clustering on OSM users and assigned a cluster to any user with an embedding, and then encoded the cluster based on the average number of edited changesets among this cluster. The idea behind `cos_sim_last_5_users` is that users who are similar to each other are more likely to edit the same objects. Starting with an edit to an OSM object, we compute the cosine similarity between the user responsible for the edit and the previous five mappers that edited the object. Next, we trained a new model by injecting the embedding features, and we have seen a relative improvement of 1.3% in our primary metric, area under the receiver-operator curve (AUC-ROC). The feature importance of `kmeans_cluster` is ranked as high as 2/49, with a coverage of 99.9%, while `cos_sim_last_5_users` has an importance rank of 16/49, largely due to a relatively low coverage of 64%, meaning that the majority of edits in OSM create new objects, so there can be no editing history for these. Because of the AUC improvements and high feature importance, Facebook has deployed this model in production to detect vandalism, as a part of the data validation in the Facebook Map and Daylight Map, a validated, vandalism-free distribution of OSM [8]. _Vandalism Corpus_ The accurately labeled dataset of vandalism to named elements in OSM is a tremendous asset to researchers hoping to further the work of automated vandalism detection. As part of the continual quality-assurance work at Facebook, teams of professional mappers are consistently labeling and improving this running list. As part of this work, we are publishing this fully labeled vandalism corpus for others in the OSM research community to use [9].

talk on conference website

The rise of organized editing practices in the OpenStreetMap community has outpaced research methods for identifying mappers participating in these efforts and evaluating their work. This research uses machine-learning to improve upon prior approaches to estimating corporate editing on OSM, contributing both a novel methodology as well as summary statistics that shed light on corporate editing behavior in OSM.

In the past five years, the OSM community has seen a dramatic rise in organized editing, including corporate, humanitarian, and educational, on the platform. These new actors have continued the ongoing debate surrounding OSM’s relationship with organized editing, with new rules and best-practices being implemented to align the interests of the organizations with those of the community. We became interested to study how the editing habits of these new actors differed from the community as a whole, but were quickly confronted by the challenge of producing accurate measures of their activities. In this paper we aim to fill this gap by creating computational methods of understanding different editing behaviours on OSM to classify editors as being corporate or volunteer. Classifying individual editors has been done in the past, on a more local level, for example in the recent analysis on editing in Mozambique. [1] Studying corporate editing behaviour, first requires a list of corporate editors. In the past, researchers have searched individual "organized editing team” webpages. Instead, our paper presents a novel method for classifying users on the platform, by scraping user profiles. There are two possible approaches to extract corporate mappers based on user profiles. The first approach uses a clustering of the keywords within the profiles. Though effective at uncovering relations between users (like students, programmers, Garmin editors, Colorado mappers), this method failed to properly capture all known corporate groups. Instead we did a keyword search for corporations listed on the Organized Editing List and classified similar users together. We then divided this list into corporate or non-corporate. This simplification was done to align with past research into corporate editing [2]. Using this extracted list, we discern features that could act as “signals” for organized editors. Explicitly, which features from the changesets can point to an editor being corporate or volunteer. Do corporate editors edit specific types of items? Do their time series signatures differ? For the creation of these features, we relied on Jennings Anderson’s past work on corporate editing for inspiration [2]. The first set of features came from OSM changeset metadata which is rich with user descriptive data like the editor used, comments, and source. We find that most organizations use editors like JSOM and iD. Next, we attempted to model which objects corporations edit by finding descriptive words like “service”, “road”, and “building” in the comments of the changeset. We observed that most corporations focus on services and roads, as opposed to buildings which tend to be dominated by volunteer mappers. The third feature was motivated by the observation that as the interests of a corporation change, the editing of its mapping team can also change. This has led to the well documented phenomena of corporate mappers having a geographically dispersed editing pattern. This is markedly different from many volunteer mappers who often begin by mapping their local neighbourhoods. Using established metrics, we calculated the geographic dispersion for each user based on the latitude and longitude of their edits. The metric we found most effective was the timeseries signature. Corporations have a traditional 9-5 mapping schedule, whereas non-corporate mappers tend to map far more haphazardly, including significant mapping on the weekend. When attempting to convert the timeseries signature into a usable metric, we came across a problem: timezones. All changesets in OSM are normalized to UTC time, this means that a user editing at 8am in Toronto, Canada and another user editing at 8pm in Beijing, China would in fact appear to be editing at the same time in OSM. Longitude and latitude data are not an effective method of extracting the mappers timezone, since editing on OSM is increasingly done remotely, through “armchair mapping”. To utilize this strong signal, we developed a new method for normalizing a users time signature, and it was based on the observation that individual corporations have several key editing patterns, depending on where their employees are located. For example, Facebook has two such patterns, each displaced by around 8 hours. This motivated us to create a “corporate editing signature” and translate the corporate signatures to find the minimal distance between the two. After using this method of adjustment, we were able to significantly improve the alignment of the time-series. In other words, we were able to recover the local time zone of most of these corporate editors. Figure 1 illustrates corporate mappers before and after adjustment. Figure 1. This plot shows how corporate time zones were recovered after minimizing distance between corporate actors and a “corporate mapping signature”. Once we realigned each user using this method, we calculated the distance between a user's adjusted time signature and the “corporate signature”. This feature ended up acting as a key determinant of the likelihood of a given editor being corporate. Out of the top 100 editors (who had the smallest distance to the corporate signature) all of them belonged to corporations. Utilizing the user features we predict whether an editor is corporate or not. We experimented with several classification algorithms, including logistic regression, k-nearest neighbours, support vector machines, and neural networks. The four most important features in the prediction task, ordered by impact on model, were the geo-score, time series score, first edit date, and the editor type. All models provided comparable results offering a high recall of 96%+ and predicting anywhere between 700 to 2,000 additional corporate mappers. Examining the newly predicted mappers reveals users that map for humanitarian groups like HOT, corporate mappers that the initial scrape didn’t pick up on, corporate mappers who reveal their association only in the hashtags, users who are likely corporate mappers with no ability to know for certain, and volunteers. We remove any “predicted mappers” who have known humanitarian associations because these users are beyond the breadth of this paper. We are now entering the stage of further validating the different models based on a manually annotated set of users that any of the models predicted to be corporate. We aim to find the model that predicts the most “corporate mappers” and the least volunteer mappers. References [1] Madubedube, A., Coetzee, S., & Rautenbach, V. (2021). A Contributor-Focused Intrinsic Quality Assessment of OpenStreetMap in Mozambique Using Unsupervised Machine Learning. ISPRS International Journal of Geo-Information, 10(3), 156. MDPI AG. Retrieved from http://dx.doi.org/10.3390/ijgi10030156 [2]Anderson, J., Sarkar, D., & Palen, L. (2019). Corporate Editors in the Evolving Landscape of OpenStreetMap. ISPRS International Journal of Geo-Information, 8(5), 232. MDPI AG. Retrieved from http://dx.doi.org/10.3390/ijgi8050232

talk on conference website

I explore the spectrum of web map rendering techniques between raster and vector, and outline new approaches towards making universal map applications with OSM.

OSM's mission is to create a free, global dataset of geodata for diverse applications. A mainstream OSM use case is **web cartography**: the graphical display of geographic features on the standardized browser platform. Cartographers want to make beautiful, labeled, multi-resolution maps; maps need to be loaded progressively for a smooth user experience. The web map status quo centers around two distinct approaches. One approach, used by the OSM Carto project, is server-rendered raster map tiles displayed as images in the browser. Another approach is to use a WebGL framework that consumes tiles of vector features. In the first part of this talk, I will investigate the tradeoffs inherent in these approaches with regards to **multilingual text and internationalization**, as these pose limitations for localizing OSM applications to different languages and cultures. The second part of this talk will introduce new techniques and libraries for rendering OSM data that are a compromise between the raster and vector map ecosystems. I'll demonstrate some of these ideas in protomaps.js, a new Canvas2D and Leaflet-based open source map rendering library.

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During the past decades, the European Commission has invested billions in research through various programmes, such as H2020. In this study, we review exhaustively all the H2020 open deliverables to analyse how these public european projects are relying on OpenStreetMap.

Since 1984, the European Commission has been supporting research through various successive programmes. Recently, from 2014 to 2020, the EU invested approximately 80 billion euros into its eighth programme, named Horizon 2020 [1]. Among various focuses such as the excellence of science or industrial secondments, H2020 emphasised on supporting an open access policy for all the research results [2]. Moreover, H2020 projects were strongly encouraged to use open source software and tools. Practically, all the research domains were eligible to be supported by the H2020 programme, and therefore, the scopes of the projects vary from e.g. computer science, to philology passing by agriculture… Technically, as these projects are almost always involving several partners located in several European member states joining forces from multiple institutions, there is often a need to deal with data coming from different places. And, more generally, geo-data are often involved to tag information which may be research data, meeting localisation, partner addresses, etc. In such a context where open source tools are recommended by the European Commission, we analyse the presence of OpenStreetMap in H2020 projects. In addition, we also review the presence of other geographic services such as Google, Bing and Baidu maps, in order to better understand how researchers tend to choose one over the other. Thanks to the open access policy, participants of the H2020 projects had to make their results available. To do so, their various types of materials were submitted to the European portal which then offers them publicly. As a consequence, for each project, one can access the articles (through DOIs), the blog posts, the slide decks, the deliverables… In particular, in our study, we decided to focus on the deliverables as they are accessible on the EC portal directly and are the common reports written by the partners to describe their approaches. Indeed, these deliverables (usually written on a regular basis during the project) report on the findings and methodology set up to achieve the project’s goals and authors explain their architectural choices in depth such as describing the tools used. As a consequence, cartographic services, if involved at some stage in the project, are likely to be mentioned in these documents either as acronyms (e.g. OSM) or as website references (e.g. https://www.openstreetmap.org/). In order to obtain the deliverables together with projects’ information, we combined two European sources of information to gather all the facets we wanted to cover: CORDIS [3] and Data.Europa [4]. In particular, we extracted from CORDIS various high-level information about the projects themselves: from their names and acronyms to their durations passing by the specific European call-for-fundings they answered and obtained their money from. This latter category can be useful in order to have a finer-grained understanding of the domains which are prone to involved cartographic services. Next in order, Data.Europa was used to download the deliverables themselves, which required several days of computing resources. Overall, during the course of the H2020 programme, 33636 projects were funded by the European Commission. Depending on the type of action which was set by the projects, not all of them had some open deliverables written (and thereby available on the Europa platform). Actually, a large part of these projects did not have deliverables per se but rather articles or web posts. We indeed counted 25157 projects without deliverables which restricted our study to the remaining 8479 projects. Out of them, we listed a total of 92612 distinct deliverables to be analysed, representing more than 260GB. Technically, once all these deliverables were downloaded, we searched them for various terms to know if some cartographic services are involved in the text. We therefore set up several regex rules (e.g. 'open.?street.?map’ or ‘[^a-z0-9]osm[^a-z0-9]’) which were run over the 92000+ deliverables. This allowed us to systematically count all the occurences of the considered cartographic solutions. In the end, we found that 1840 deliverables (from 651 projects) mention OpenStreetMap. More precisely, through all the H2020 deliverables, there are approximately: 18600 mentions to OSM, 2800 to GoogleMaps, 226 BingMaps and 4 to BaiduMaps. Empirically, we notice that 1) one order of magnitude separates the occurrences of each cartographic service and 2) OpenStreetMap is from far the most represented solution and thereby the one on which public European researchers rely the most. Contextually, it is also interesting to note that not all the deliverables (1796 of them) mentioning “point of interest” refer to a cartographic service. Moreover, we also analysed the co-occurence cases, where different cartographic providers are jointly mentioned within a single deliverable. Notably, there are not that many. Indeed, only 59 deliverables mention both OSM and BingMaps, over the 226 occurrences of the latter; and only 291 deliverables mention both OSM and GoogleMaps, over the 2800 occurrences of GMaps. Besides, only 39 deliverables mention OSM, GoogleMaps and BingMaps. Such figures tend to suggest that once a group of researchers has chosen a cartographic solution, they tend to stick to it and do not try to compare them. Furthermore, regarding OpenSeaMap, we counted 312 mentions from 27 deliverables, among which 20 ones mention both OSM and OpenSeaMap, showing how connected are the two initiatives. In this study, we systematically analysed all the available H2020 deliverables, searching for cartographic service references, with a specific focus on OpenStreetMap. Our efforts show that OSM is the most used cartographic service in European H2020 projects in terms of mentions in the deliverable’s texts, followed by GoogleMaps with one order of magnitude less mentions. It is worth noting that these projects involving OSM were backed by almost 4 billion euros of public money. Based on these first interesting results, we plan to extend our scope of analysis following three axes. First, we think that it could be worth reviewing also the other types of project’s results such as the articles or the software source code bases. Second, we hope our approach paves the road to similar reviews of public funded initiatives, and based on this observation we plan to apply our scripts to other European funding programmes. Third, additional cartographic services could also be integrated into our pipelines such as ApplePlans or other OSM-related initiatives like OpenCycleMap in order to extend the covered scope. Finally, for reproducibility purposes, we also share on a public github repository [5] all the scripts necessary to download the deliverables and generate the statistics. Furthermore, https://dgraux.github.io/OSM-in-H2020 provides the reader with additional and detailed analyses together with visualisations, hoping these will help the community better understand the impact of OSM within the public European research landscape.

talk on conference website

Since now 10 years, the Overpass API allows to query the OpenStreetMap data. This is reason enough to show how OpenStreetMap and the Overpass API have changed over that timespan. I will present and overview over user statistics, technical challenges and all kinds of requests to me as the developer.

Since now 10 years, the Overpass API allows to query the OpenStreetMap data. This is reason enough to compare the OpenStreetMap of then to the OpenStreetMap of now and to show how OpenStreetMap and the Overpass API have changed or not changed over that timespan. For this purpose I will present an overview: With which requests are the public instances of the Overpass API actually used? The Overpass API has been intended for compound queries for the public transit data model of OpenStreetMap, but actually got popular for the simpler tag queries on bounding boxes. The provisions for an area data type have never materialized. How has it protected itself and how does it now protect itself against excessive load? I have always been proud that the service runs on ordinary server hardware and nothing huge, but still can give every user a meaningful amount of computation time. The necessary measures to curb the greedy and the stupid have evolved over time. Which requests for features and questions have been addressed to me as the developer? Basically everything we use now has been requested by users of the service. Probably the best idea has been to change from the longish XML based query language to the now much shorter QL based language.

talk on conference website

Describes current situation of people using and appreciating OpenStreetMap data without being aware at all about its source. Describes how it violates OpenStreetMap license and proposes to start enforcing it.

OpenStreetMap is not really known among people. But it does not mean that they are not using it. I was talking with a pair of tourists using maps.me to navigate. They commented that OpenStreetMap sounds like an interesting project. But it seemed to them to be an unnecessary duplicate of maps.me with its great maps. They were completely unaware that all useful data displayed by maps.me is from the OpenStreetMap. OSM data can be used by anyone, with few requirements. One of them is requirement to clearly and prominently state source of data. Despite this, many companies somehow fail to include a proper attribution. Including ones that are incredibly wealthy and ones that put massive effort into designing their applications. How can we change that situation? Many people who would contribute are unaware that OpenStreetMap exists. We are losing potential contributors. Especially among people not interested already in open data and maps. It is one of reasons why demographics of OpenStreetMap are so diverged from overall population. One needs to be quite unusual to be even aware that it exists and that this data is widely used. Due to obvious conflict of interest between OpenStreetMap community and corporations using this data fixing this problem will not be easy. But we should try.

talk on conference website

In this workshop, I will assist you on how to create your own MapComplete theme.

MapComplete is an easy to use web editor, suitable for desktop and mobile. It can be seen as the spiritual offspring of both StreetComplete and MapContrib. This workshop is a follow-up of my talk about MapComplete, and it aims to give an introduction in how you can create your own theme with MapComplete. In this format, you will be provided an introductory tutorial video and the necessary documentation which you should be able to follow along. I am standby in a video conference, so that questions can be asked live. This workshop serves three goals: - I want the community to learn how they can create and share there own thematic maps/editors - I want to have some more, qualitative themes into MapComplete (and have some more contributors) - I want to test and improve the documentation If you want to join, you: - will need to have made at least 500 changesets with your OpenStreetMap-account - will need a thorough understanding of how tagging works in OpenStreetMap - will need some knowledge on how to edit a .json-file - will need to have access to a computer - will need what MapComplete is, have tried it at least once. My other talk is a good introduction

talk on conference website

Global dataset and a map style for hiking and biking developed from OpenStreetMap - that's MapTiler Outdoor. During development, we have to solve multiple challenges originating from a lack of international standardization in trail marking.

MapTiler Outdoor is a global dataset and a map style for hiking and biking available as a zoomable and web-compatible vector tiles, which are ready for use in OpenLayers, MapLibre, Leaflet, QGIS as well as mobile applications. After first excitement, challenges starts to appear one after another: most of them originating from a need to create a rigid set of rules for the entire world, which is full of diversity due to the variety of nature. We also faced the challenge of lacking international standardization for hiking trails, which is unique for each country (and even on this level with many exceptions and specialities - like oneways, via ferrata routes or even climbing trails). And last, but not least, we have to dig all this information from the OpenStreetMap and turn it into a map understandable for everyone. We managed to create a layer with trails and corresponding points of interest, which can be filtered on hiking or biking in the customize tool. This layer can be overlaid on top of any map. However, we created a specialized map style which combines OpenStreetMap with contour lines and hillshade and highlights things you need for moving around in the nature. The schema of MapTiler Outdoor is based on the open-source OpenMapTiles (OTM) schema and data are processed using OMT stack. MapTiler Outdoor is available via MapTiler Cloud as a service or in a form of a data package for download.

talk on conference website

A/B Street is an open source traffic simulator built on OSM and public census data, easy for the general public to use. This talk will cover some case studies of A/B Street being used to advocate for cycling infrastructure in Seattle, and describe how to use it anywhere.

A/B Street (abstreet.org) is an open source traffic simulator built on OSM and public census data. It simulates car, bicycle, foot, and public transit traffic, and runs on Mac, Windows, Linux, and directly in the web browser. A/B Street allows the user to reallocate existing road space between cars, protected cycle lanes, transit-only lanes, and street parking. Users can also modify traffic signal timing and create access-restricted neighborhoods. Individual and aggregate results from the simulation can be compared before and after the changes, creating a simple way to evaluate potential changes. A/B Street has been designed for the general public to easily explore proposals for reducing dependency on cars. This talk will cover some specific cases in Seattle where the software has been used to propose real changes, like opening a shortcut through a gated community for cycle and foot traffic to avoid dangerous roads. We'll discuss how to start using A/B Street in your area, the challenges in finding other open data-sets required, and some options for how to publish results. Finally, the talk will briefly demonstrate how A/B Street's rendering can be used for validating some tags, and how to get involved with the project.

talk on conference website

SotM Working group say thank you to all volunteers and attendees and good bye until next year.

SotM Working group say thank you to all volunteers and attendees and good bye until next year.