State of the Map 2020

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Welcome to SotM 2020!

Welcome to SotM 2020! Some words about how the virtual SotM will run.

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OSM Foundation board chairperson Allan Mustard offers his personal assessment of challenges facing OSM and how he thinks the community and the OSM Foundation Board could deal with them.

OSM Foundation board chairperson Allan Mustard offers his personal assessment of challenges facing OSM and how he thinks the community and the OSM Foundation Board could deal with them.

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Last year, HOT collaborated with OSM Liberia and iLab Liberia to complete the mapping and perform quality assurance in four counties in Liberia. The objective of this program is to update OpenStreetMap data to assist with a Social Registry data collection in four counties in Liberia: Bomi, Bong, Nimba and Maryland. Buildings and roads are the entities to map for this program.

The Government of Liberia is working on building an effective social protection by implementing a Liberia Household Social Registry (LHSR). The data collection for this Liberia Household Social Registry (LHSR) requires house-to-house visits. To ensure that the data intake questionnaire is administered to all households, updated map is needed since it’s last updated in 2008. HOT collaborated with OSM Liberia and iLab Liberia to complete the mapping and perform quality assurance in four counties in Liberia. The objective of this program is to update OpenStreetMap data to assist with a Social Registry data collection in four counties in Liberia: Bomi, Bong, Nimba and Maryland. Buildings and roads are the entities to map for this program. The mapping itself is done by local university students. After completed mapping all four counties, they perform data validation using JOSM. Then, the data is being validated again by OSM Liberia, iLab Liberia and HOT ID. Lessons learned from this digitization program are: - First, it’s really great to involve local mappers because they have local knowledge especially on mapping roads. The road’s classification in Africa is quite different, they even have their own OSM Wiki Page. - Second, it’s really useful to review the works of mappers while they’re mapping, so that we can point out if there is any mistake. We can also make a guideline out of this mistake we found, so that they don’t do it again in the future. - Last, it’s better to make new mappers get familiar with mapping first before jumping to data validation. The longer duration of mapping, the better validator they would become. Though it can be helped by providing a validation guideline, it’s better for them to understand the do’s and the dont’s, what’s right or wrong, what’s supposed to do and not to do when mapping basic objects like buildings and roads. To grasp this level of understanding requires some time of mapping. We still find warnings, errors, conflicts and unfixed geometry in the tasks validated by local university students. Yes, this world would be better if there are more validators, but it takes some mapping time to be a good validator, to understand what needs to be corrected and what’s not.

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Drone technology is an evolving industry used in multi-disciplinary fields ranging from agriculture, marine conservation to real estate and films. There’s so much potential and possibilities on what drones could help people making work faster and more efficient. In mapping, aerial imagery uploaded to Open Aerial Map can be used as basemap for OSM editing. Example cases are in Batad Rice Terraces, UNESCO Heritage site for mapping tangible changes in the community over time and create a tourism map and other small urban communities in the Philippines, aerial imagery was used for mapathons and community mapping done in OSM.

This talk is about sharing my experience as a drone pilot here in the Philippines and what contributions in OSM it can do if given more opportunity in the future to fly drones in remote areas in the Philippines. Only 8% of certified drone pilots in the Philippines are women. It’s been my advocacy to encourage women to be drone pilots. In Batad Rice Terraces, a UNESCO world heritage site, there has been a massive influx of tourists coming to visit. Using a drone, I was able to acquire high-resolution imagery in 2019 for the community. The research team was able to use the image to detect tangible changes in the rice fields and houses are no longer built traditionally (from coconut trees and lumber to cement and galvanized iron sheets). The image, DTM, and DSM were uploaded in Open Aerial Map and been used in OSM as a base map to map out the recent community. Other OSM PH drone imagery collection initiatives area in urban areas such as Lupang Arenda in Taytay, Rizal, and Downtown, Dumaguete. The images acquired from these areas were uploaded as well in OAM and used as base maps in the local community efforts to map out their city and small mapathons conducted in Manila.

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The talk is to basically share the experience of working with Imports of Health Facilities in India (OpenGovernmentData). We planned to go briefly through the background of Open GovernmentData and the countries following the OGD, along with the compatibility of OdBL for the datasets they provide to the public. The main purpose of the import is to provide accessible data of accurate health care information from the Open Government Data directories for Hospitals, Health facilities, Blood banks, Health Centers and Health Clinics information which can be useful for all the people and also the Humanitarian team in India. The primary focus of the talk is the import process from data preparation to the execution which includesImports Guidelines , Data Cleanup, Data transformation, Data Execution. The talk shares the detailed stats of the - Indian Health facilities OSM map data coverage before & after Imports and our survey experience for collecting the health facilities records in our region - Telangana. We also line up with the Survey experience to collect the Health facilities records and conducted OSM awareness programmes. Conclude the session with the future plan and local community support.

We RMSI Team is performing the imports of Healthfacilities in India for OpenGovernmentData. We want to share the experience working on our successful imports from planning to importing the data. Also want to share the usage of Open Government Data and the other countries who are following the same. In total we have 190k+ records in the datasets which have been provided by the government of India and among them 46k+ are already on OSM. he talk is to basically share the experience of working with Imports of Health Facilities in India (OpenGovernmentData). https://wiki.openstreetmap.org/wiki/India_Health_Facilities_Import The main purpose of the import is to provide accessible data of accurate health care information from the Open Government Data directories for Hospitals, Health facilities, Blood banks, Health Centers and Health Clinics information which can be useful for all the people and also the Humanitarian team in India. The primary focus of the talk is the import process from data preparation to the execution which includesImports Guidelines , Data Cleanup, Data transformation, Data Execution. The talk shares the detailed stats of the - Indian Health facilities OSM map data coverage before & after Imports and our survey experience for collecting the health facilities records in our region - Telangana. The talk is totally based on the knowledge sharing on Imports and how to improve the base map data in India.

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This talk will present an evaluation of different OSM routing software and its impact on the journey. Several popular OSM routing software results will be evaluated on its route-cost (distance and or time) and different modes of transportation (from walking to motor-vehicle). Additional evaluation on the ground will also evaluate which route is a good (or bad) one and what may cause this impact.

There have been multiples OSM routing services that are available, from offline to online. While this situation gives people many options to choose from it may also confuse people on which service they should choose or use. This talk is an effort to evaluate these services on different modes of transportation. The evaluation is not only on the routing result but also on when the route is used in real-life scenarios. It tries to answer practical questions such as what is the routing service that provides the best result for walking? Or is it better to use Graphopper or OSRM for car navigation? Different OSM routing services will be evaluated using several start-end points and what do the results look like. How do different routing service results in shorter or longer distance/time and what may have caused it? Surely the journey in the real situation will be presented as well. While the answers highly depend on several factors (such as road tag completeness, local road rules, and time of traveling), this talk will be a good overview for the audience that wants to know more about routing services in OSM. No technical knowledge is necessary.

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This talk is based on the experiences I faced while working with the public sector on mapping and try to identify some of the impacts that made and answering 3 questions which arise: * How much Money should I spend? * How much Accuracy do I need? * How much Human Involvement?

In this talk, I will be explaining experiences faced while working on some of the major projects in public sector related to FOSSGIS and where all we could implement OSM into. Some of the major projects are as: ##### Utility mapping of State Electricity Board: Comprehensive solution for mapping was developed for Kerala State Electricity Board. The solution included a. Mobile field survey and mapping tool b. Map editor for post production and drawing c. Map viewer d. Map printing e. various intermediate data processing and conversion tools. Complete tool chain was designed and field tested. KSEB used our approach to map 11Kv electric poles of distribution department. This covered around 14 Lakhs electric poles. The original cost estimate for the work by KSEB using traditional approach and proprietary systems was around Rs 200 crores. Technological solution developed by ICFOSS will dramatically reduce cost of mapping and lead to substantial saving of public exchequer. The benefit through utilisation of the map data like electricity loss minimisation and quality improvement is also to be noted. For the original price, work would not have happened. The entire process was based on technologies and standards around OpenStreetMap. ##### Participatory mapping of Govt. Offices Government of Kerala aims to build a spatial database of whole Kerala based on OpenStreetMap. A participatory model was planned for the field survey and the execution was planned in different phases; Government Institutions mapping, Road and Waterways mapping, etc. As preliminary phase, govt. Offices mapping was done by developing an HTML5 based Laravel web application. And this will be later on validated and uploaded to OSM. ##### Route analysis for Govt. Fiber Optic Network A dedicated Optic Fibre Network (Kerala state Fibre Optic Network) connecting 30000 of Government Institutions including schools and other departments was planned. Substations of KSEB (Kerala State Electricity Board) across the state were chosen to create the backbone network installing PoPs (Point of Presence) at each substation. As part of the planning phase, total length of fibre cable required was to be found. Based on GIS based routing techniques and derived methodologies, cluster of networks and total length estimate were identified. ##### Canal digitisation within 42 sq.km. paddy area Government of Kerala aims to map canal system within Thrissur Kole Wetlands an area of around 4300 hectares to measure total water holding capacity of the system. A drone based aerial survey model was planned for the data collection and further digitisation and volume measurements were done using OpenDroneMap and OpenStreetMap based toolchain.

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Separation of features by their importance is a core technique in cartography. But what happens if features of the same feature class (e.g. train stations) have a varying importance? A manual cartographer can choose the important features based on other knowledge. Howerver, rule based cartography which is dominating in the OpenStreetMap environment cannot work this way. A computer programme rendering a map needs selection rules. Someone has to implement them. This talk presents some examples showing how to add lacking relevance information if the importance of features within one feature class ranges span between extreme values. It will show how train stations and airports can ranked using other OSM data and external datasets.

Maps cannot show all information at all scales. A cartographer needs to decided which features should appear at which scale on the map. OpenStreetMap based web maps are usually products of rule-based cartography, a computer programme selecting features from a data store and rendering them. It needs rules. If all the features of a feature class are of similar importance, the selection is simple. If not, ranks or attributes to be used for ranking are required. Ranks are assigned to roads as values of the `highway=*` tag, rankable attributes are available as `population=*` tags in OpenStreetMap. In some cases some features of a feature class are of high importance but many other features of that class are irrelevant at medium or lower scales. For example, a map on a medium scale cannot show all stations or all airports in a country. Some airports are only used by a few flights per day, others are not even public. Some train stations have dozens of trains per hours while others get served only few times per week or are used by a dozen passengers per week. Important features should appear at country level but irrelevant features should appear at high scales only. This challenge has not been addressed by map styles used for OpenStreetMap data so far. Authors of map styles circumvent the issue by rendering all features at higher scales only or by selectiong features randomly until enough features are rendered. The talk will present how to add ranking information to train stations and airports from OpenStreetMap using other OpenStreetMap data and/or external datasets. The ranking is applied in the rendering database. The ranking train station is based on the presence of public transport route relations and has been used for train station rendering of OpenRailwayMap's infrastructure map style since January 2020.

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Crowd2Map Tanzania is a volunteer run crowdsourced project that has been mapping rural Tanzania since 2015. This talk will give an overview of some of the lessons learnt, particularly in building mapping communities in remote rural areas with first time smartphone users.

Since 2015, we have been adding schools, hospitals, roads, buildings and villages to OpenStreetMap with the help of over 12500 volunteers worldwide and 600 on the ground in Tanzania. With minimal budget and no staff we have so far added over 4.1 million buildings and trained community mappers in 26 areas of Tanzania. We were asked to run a mapathon at the United Nations General Assembly in October 2018 and have also been featured in many newspaper articles. We have prioritised mapping those areas of Tanzania where girls are at risk of Female Genital Mutilation, FGM, so that activists and the police can more easily find and protect them. But we have also been working with other community groups and district officials on how maps of their area can help with development. The mapping is in two phases – firstly online volunteers trace roads and buildings from satellite images then volunteers on the ground add names of villages, offices, churches, shops and other points of interest using a free smartphone app Maps.Me and we then produce printable maps of villages, wards and districts, and train people about how they can use these maps. Training field mappers who have never been online, seen a map of their village, or used a smartphone before has multiple challenges. This talk will outline some of them, and lessons learnt, and seek to stimulate a discussion from the wider community on sharing good practice and resources in this area.

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Lightning Talk session.

## Kenya Covid-19 Tracker *Zacharia Muindi* Map Kibera has launched the Kenya COVID-19 Tracker Ushahidi Instance to document geospatial data, map out resources and services being offered to the community in response to Covid-19 and track cases across Kenya. The project will improve OSM basemaps in Kibera and other informal settlements of Nairobi to allow visualisation of current status and hotspots. ## Mapping Gender issues in Kenya *Caroline Akoth (WiGIS, Kenya)* This lightning talk will cover Women in GIS, Kenya efforts to map gender issues in Kenya with support of our community, through #DataViz Challenges. ## Where can you dine like a king? *Stefan Keller* - [Whitepaper](https://md.coredump.ch/s/H1IQbLzjU), [Slides](https://www.slideshare.net/StefanKeller/where-can-you-dine-like-a-king) Synchronizing Local Datasets and OpenStreetMap using QGIS. ## Nurturing a Ministry of Mapping *David Garcia* Who cares for the mapmakers who care? I would like to share a story about a geospatial collective about OSM and FOSS4G that we have been trying to organise in the Philippines during the pandemic. ## Building the Street View Experience, Lagos, Nigeria *Ayo Akinseye* - [Website](http://www.moriwo.com/), [Slides](https://digitalspatial.com/sotm2020/presentation/) For better Geo visualization, we built a website with Geo referenced 360 degree panorama photos. Lagos, Nigeria is our first phase. ## What's new on OSMCha? *Wille Marcel* OSMCha is a validation tool for OpenStreetMap. On the last months we added some new features like visualization of relation members and more options of background imagery. Get updated on OSMCha with this talk. ## [GeoLadies PH](https://www.facebook.com/geoladiesph/) *Arnalie Vicario* Brief talk about GeoLadies PH, which was inspired by Maning Sambale's "How Do We Change the Ratio?" presentation in 2014. GeoLadies PH dvocates incommunity diversity, collaborative participation and affirmative spaces forwomen and under-represented communities in OpenStreetMap and the localgeospatial science community. ## Fighting FGM (Female Genital Mutilation) with Maps *Janet Chapman*

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Mobile mapping is the process of collecting geospatial data from a mobile vehicle using a 360º camera, laser scanner, GPS/IMU positioning system, and other sensors. This is one of the most time and cost effective methods to collect geospatial data, but the required equipment can be expensive. An alternative approach to mobile mapping brings the opportunity to the OpenStreetMap community to "Map in 360" using a wide range of consumer devices compatible with Mapillary. Instead of capturing a frame of street-level imagery in a single direction, a 360º camera can capture the entire scene simultaneously. 360º street-level imagery provides the OpenStreetMap community a more comprehensive understanding of the map space and more accurate machine-generated map data. In this talk, we will review the workflow of data collection with 360º cameras, look at how to upload 360 street-level images to Mapillary, and compare the impact of different 360º camera models in terms of map data extraction accuracy. OpenStreetMap communities can use this knowledge to coordinate their own street-level imagery collection with 360º cameras to contribute to Mapillary and improve local maps.

Mobile mapping is the process of collecting geospatial data from a mobile vehicle using a 360º camera, laser scanner, GPS/IMU positioning system, and other sensors. Mobile mapping is a time and cost-effective way to conduct asset inventory, monitor road infrastructure, and map street furniture. However, the high cost of acquiring mobile mapping equipment, such as professional 360° cameras and laser scanners, inhibit the ability of local OpenStreetMap communities to leverage mobile mapping technologies. With advice from Mapillary, low-cost devices that are accessible to communities make mobile mapping possible on a wider scale. 360º cameras may be used in different vehicle types: cars, bicycles, quad bikes and also on foot. With a 360º field of view, these cameras capture everything around the sphere. Where flat frame images can limit the map editing workflow, 360º images bring panoramic viewing to OpenStreetMap editors for a more comprehensive understanding of the space and place. The Mapillary photo overlay supports panorama view on OSM ID Editor and JOSM. After images have been captured and uploaded to the web, Mapillary creates a reconstruction of the scene in 3D using an open-source Structure from Motion library (OpenSfM). This helps to improve the image GPS positions, based on the overall consistency of the 3D scene. Next, the objects in the imagery (such as traffic signs and other types of objects) are identified by computer vision. Combining detections of an object in several images and the aforementioned 3D model allows us to calculate the position of the detected object in the world. Overall, this talk will examine the recommended 360º cameras, discuss data collection and benefit of street-level imagery collection with 360º cameras, review strategies for uploading 360º images, comparing different 360º camera models, and highlight how OpenStreetMap communities around the world are using 360º images to improve local maps with Mapillary.

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This talk will present the results of a survey done on the State of the OpenStreetMap, the unique challenges and success of OSM in Africa and how the different communities are working together to grow the map and the community on the continent.

The global State of the Map conference is taking place in Africa for the very first time, and it is a great opportunity to light a torch into the state of OpenStreetMap in Africa. What is happening around the Map the West, Central, South and North of Africa? Where are the active communities, what are the reasons for their success, what challenges are they facing and what solutions are they using to overcome the challenges? This talk will present the results of a survey done with different OSM communities in Africa to answer the above questions, while at the same time looking at the map data to measure the level of completeness in each country, and examining the reasons behind the different scenarios in the different countries. The talk will also present the views of OSM Community leaders from Africa on how to promote sustainability, resilience, inclusiveness, diversity and equity in decision making in regards to the future of OpenStreetMap. Why are there very few official local OpenStreetMap Chapters from Africa and what can be done to change the situation?

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What are the perspectives and challenges around the new wealth of building data in OpenStreetMap?

OpenStreetMap (OSM) is constantly expanding into emerging fields, particularly around building datasets. Through its participatory character, OSM provides an incomplete, but global coverage of buildings, nowadays already outnumbering roads. Their distribution and properties are the best indicator to population density, economic activity patterns, and risks of natural hazards. Thus, OSM constitutes a perfect base to provide answers to many related questions of various stakeholders from communities, governments, private sector and research. We present an overview of how worldwide building data is used and will be in the near future. We will guide through our latest projects, reaching from the creation of risk models, via automated building extraction from satellite sensor data to building completeness estimates, and set these developments in context with other community-driven activities. We report on the challenges of managing global building data, and combining attributes from other open and public domain big datasets towards rich handling of building data in and around OSM. A special focus is on the ways how this new information can be made useful to support mappers to achieve higher completeness of building data.

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Le Togo, l’un des plus petits pays de l’Afrique de l’Ouest abrite depuis 2013 une communauté OpenStreetMap. OSM Togo a mise en œuvre de nombreux projets mais a connu également des difficultés. Cette présentation a pour objectif de faire un aperçu sur la vie de la communauté OpenStreetMap Togo.

OpenStreetMap (OSM) considered as the wikipedia of cartography is a Collaborative Mapping Project which exists in several countries around the world. So, Togo, one of the smallest countries of West Africa, has been hosting for more than five years a strong and dynamic OSM community called OpenStreetMap Togo. Several activities, mainly mapathon sessions, mapping parties, and capacity building trainings have been carried out through several projects for various target groups including students, public institutions, civil society organizations, and companies in order to promote the OSM project and to have a quality data on Togo. Highlights of these projects are: "Digital mapping OpenStreetMap as lever for local development in Togo", “GirlsMap, a gender-oriented project implemented in several African countries”, “G roads project (mapping of mainly roads of Togo)”, and “mapping of all pharmacy of Lomé, capital of Togo (CARTOPHARMA)”. Today, there are more than 200 local contributors who, through their actions, are making the OSM data evolve at the country level. However, like in most OSM communities in Africa and around the world, OSM Togo is sometimes constrained by several difficulties that limit the expansion of the OSM project and the production of better quality data in Togo. The objective of this presentation is to highlight the life of the OSM community in Togo, its projects, strengths, challenges and its difficulties related to quality OSM data production.

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We built open-source infrastructure that allows the community to integrate open data reviews of POI into the OpenStreetMap ecosystem. This enables any application or website to make use of a reviews layer, and to benefit from the shared data pool that is created by a combined user base of participating applications. We built it to ensure that people all over the world can freely share their insights about things that matter to them without being confined to proprietary data silos.

Mangrove is a non-profit initiative to create a public space on the Internet where people can freely share insights with each other and make better decisions based on open data. Our goal is to create an Open Data Ecosystem for Reviews of places, companies, websites, books, and more. We built open-source infrastructure that enables any application or website to integrate a reviews layer, and to benefit from the shared data pool that is created by a combined user base of participating applications. In this talk, we are going to introduce to the OpenStreetMap community the technology that is available, and we are going to show how OSM-based projects could benefit from an integration with Mangrove. Furthermore, we are introducing the non-profit Open Reviews Association, ORA, who became the custodian of the Mangrove technology and open dataset. We invite anyone to join as a member in order to shape the direction of the project and help achieve its vision.

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Humanitarian Openstreetmap Team (HOT) is an international team focused on humanitarian action and community development through open mapping. Since 2010, the organization has managed activations to attend multiple events such as understanding hazards, public health, refugee response, among others. However, when we think about organizing large-scale efforts it can be complex, due to the necessary logistics, volunteers involved, and also assuring that the data collected is meaningful. MapCampaigner, is a tool which monitors progress, view metrics on the quality and completeness of collected data and users engaged. The goal of this talk is to present the latest features included in the latest update of the tool, the goals that MapCampaigner accomplishes and many humanitarian and non-humanitarian examples through a demo.

When we talk about a mapping campaign, it generally has specific characteristics: Desire to collect geospatial data for a purpose. Define an area of interest. Define a set of features and attributes to collect. The period of time of the campaign Group of people to collect the data. Could be highly trained staff members or volunteers. It would sound simple. However, it is possible to find challenges in difficult places. For instance, how to crowdsource precise locations and specific attributes of every school in Barranquilla, Colombia? Because some locations are inaccessible, it becomes difficult and costly to deploy people to the area to collect and validate the data. Many tools have been developed to address these challenges remotely, like OpenMapKit and Maps.me to collect data and do some quality assurance with JOSM, for instance. Despite this, these tools do not have the property to monitor specific features/attributes, define current status of the mapping campaign, or validate people contributing in an area. Putting these challenges in mind, HOT designed the MapCampaigner tool. Users can track and measure the quality of many features such as schools, buildings and cafes at the same time. This mapping campaign will report users involved, missing tags, and percentage of completeness in an area for each attribute collected. Also, MapCampaigner has integrations with apps such as Maps.me and OpenMapKit In this talk, I will walk through the tool, how it is used, and seek input for enhancements. Will show new features included in the latest user-centered redesign using live demo. The schedule of this talk is the following: Introduction and problem statement (5 minutes). MapCampaigner features (5 minutes) Examples and demo (5 minutes) Conclusions and future work (5 minutes)

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When people come to OpenStreetMap for the first time, their expectations are sometimes at odds with what the OpenStreetMap community is doing. If you have been puzzled by an OSMer telling you that OpenStreetMap is not a map, that openstreetmap.org is not aiming to compete with Google Maps, or by their stubborn refusal to remove a private trail from the map, then this talk is for you. It will explain the basic tenets of the OpenStreetMap community and how they apply in practice.

This talk will explain some of the often-heard but little-documented basic concepts in OSM, like * we are not a map (but a database) * the map does not matter (the community does) * openstreetmap.org is not aimed at the public (but at mappers) * we map what's on the ground (not what the government or the landowner wants) Building on that, the talk will also outline why the OSM community is often skeptical about filling an empty map with data imports, about AI contributions, or about automatic edits, and why OpenStreetMap is not a business directory. The plan is to explain the basic ideas and give examples of their effects for data users or for everyday mapping practice. Where the concepts are controversial or subject to discussion, these controversies will be mentioned but not followed in depth. This talk will focus on "traditional" values in OpenStreetMap because they are omnipresent. After hearing these explanations, newcomers will have a better chance of understanding where people come from when they say things like "we are not a map", and will be better prepared to form their own opinion.

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Current presentation aspires to contribute to an overall assessment of the OSM map in Athens, Greece. The OSM content is assessed in terms of completeness and precision. Various official mapping sources and ground truth data are employed in order to measure the current state of the map.

Current research aspires to contribute to the assessment of the OSM map in Athens, Greece in terms of completeness and precision. The researcher chose Athens as, according to international published research regarding the phenomenon of Volunteered Geographic Information, it is initially assumed that it will have the maximum quality level, as Athens is the most populated city of the country. The analysis includes mainly quantitative evaluation methods. To the level that access to editable - data is feasible, various GIS techniques are employed while in other cases the assessment is performed through samples in various regions of the municipality. While evaluating, all the known properties and characteristics of Volunteered Geographic Information are considered. Eventually, a short discussion related to similarities and differencies from other published OSM assessments in other countries, completes this short initial presentation. Apart from official mapping sources, ground truth data, collected through the use of in-car GPS devices, in certain areas of the city, are providing valuable insights regarding the level of quality. The research focuses on certain geographic entities, including the geometry of the road network, the street naming and addressing, various administrative units, some building footprints, parks and POIs.

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The talk will focus on the Turkish law egulating the acquisition, collection, dissemination and trading of spatial data falling within the responsibility matrix of Turkish National Geographic Information System, effective since February 20, 2020. With the law, acquisition, collection, dissemination and trading of spatial data which is defined within the National Spatial Data Responsibility Matrix by third party individuals or legal entities are subject to prior application fees and approval of the Ministry of Environmental and Urban Affairs. The talk will reflect and report the developments in Turkey after the law, effects and implications drawn focusing on the national spatial sector, OSM, and the Turkish OSM community.

On January 30, members of the Turkish parliament voted in favour of the proposed amendment to the law regulating the acquisition, collection, dissemination and trading of spatial data falling within the responsibility matrix of Turkish National Geographic Information System. The law has officially been put into practice on February 20, with nationwide uncertainties on how it will be enforced, and on what level. With the law, the sole responsibility and authority on the national spatial data index is given to the Ministry of Environmental and Urban Affairs. Acquisition, collection, dissemination and trading of spatial data which is defined within the National Spatial Data Responsibility Matrix by third party individuals or legal entities are subject to prior approval of the ministry. Moreover, the approval will be subject to a fee of 25₺ for native, 50₺ for foreign parties per each 1/1000 plan corresponding to the study region(s) from the national topographical grid. The data layers which are included in the national spatial data responsibility list is as follows: 1. Coordinate Reference Systems and Geographical Grid Systems 2. Administrative Units 3. Geographical Names 4. Cadastre 5. Buildings 6. Addresses 7. Elevation 8. Orthophoto 9. Transportation Networks 10. Hydrography 11. Geology 12. Land Cover 13. Land Use 14. Soil Types 15. Protection Areas 16. Natural Risk Regions 17. Infrastructure 18. Energy Resources 19. Mines 20. Public Health and Safety 21. Populaiton Demographics 22. Environmental Monitoring Facilities 23. Industrial Facilities 24. Agricultural Facilities 25. Public Administration Regions 26. Flora and Fauna 27. Habitat Zones 28. Biogeographical Zones 29. Sea and Saltwater Regions 30. Atmospherical Data 31. Meteorological Data 32. NUTS Data The talk will reflect and report the developments in Turkey after the law, effects and implications drawn focusing on the national spatial sector, OSM, and the Turkish OSM community.

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This talk with survey the bright and dark history of companies and professional involvement in OpenStreetMap, lay out the challenges that we face now, and chart steps forward to figuring this out together. I want to reset the vision of the position of companies in OSM, starting by connecting back in time to when it was all more fluid in our community. Only later did some draw a sharp distinction between volunteer and professional activities in our project. The reality of the relationship of companies and professionals in OpenStreetMap from the very earliest days until today is ... complicated. There's incredible mutual benefit and purpose. There are super hard issues to address when large amounts of resources are mustered, among the constellation of many kinds of actors and motivations in OpenStreetMap. The reality is that OpenStreetMap is transformative, and that companies in OSM first come for the data, may fumble along the way, and stay for the shared mission to change how maps are made in the open.

I want to reset the vision of companies place in OSM, starting by connecting back in time to when it was all more fluid in our community. For example, a month after I met Steve Coast in 2005, I was setting up meetings with Google. Helped secure Yahoo! maps imagery in 2007. Companies hosted many of the early days mapping parties. Professional cartographers were among the projects most original enthusiasts. The point was to change how mapping was done -- including and especially at companies. Only later did some draw a sharp distinction between volunteer and professional activities in our project. The reality of the relationship of companies and professionals in OpenStreetMap from the very earliest days until today is ... complicated. There's incredible mutual benefit and purpose. There are super hard issues to address when large amounts of resources are mustered, among the constellation of many kinds of actors and motivations in OpenStreetMap, whether they be hobbyists, developers, students, researchers, non-profits and on and on. The reality is that OpenStreetMap is transformative, and that companies in OSM first come for the data, then stay for the mission. There are of course fumbles along the way. But as a community we've lacked a way to take a clear eyed view of the challenges and vital role companies and professionals have played in what OSM has become today. In part that fault lies with companies themselves, who are risk averse to delving into our wild and wooly communications. Those that do, can take a lot of heat. This talk with survey the bright and dark history of companies and professional involvement in OpenStreetMap, lay out the challenges that we face now, and chart steps forward to figuring this out together.

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Map Kibera has been working for the past two years with some of Kenya’s county governments to create maps of their primary features and funded projects. After implementing a Participatory Budgeting process, these counties realized that without good maps it was difficult for people to not only allocate resources, but to work with citizens to identify needs and prioritize funds. Map Kibera has been assisting counties to map key features and projects in OSM by working with youth from the local communities. The maps not only serve to connect citizens to the budgeting process and hold county government accountable for the funded projects, but, they have also become central to county functions in all areas. This talk will share all about the process used and outcomes.

Map Kibera has been helping communities map out their local projects by collecting data and creating digital maps that they can use for planning and decision making. The Participatory Budget Mapping project was conducted in 3 counties in Kenya: West Pokot, Baringo and Makueni. These counties had already been part of an annual process of participatory budgeting, with locals weighing in on how budgets should be spent in their counties. In early 2018, Map Kibera along with partner GroundTruth Initiative began working with the World Bank in Kenya to initiate Community Participatory Mapping, by training the local citizens on how to map their county-funded projects using OpenStreetMap, Open Data Kit, and Kobo Toolbox. The project also enabled citizens to track the progress and quality of those projects, allowing them to hold the government accountable for delivering what had been promised during the budgeting sessions. The results of the mapping are displayed on a dedicated website and printed maps for budgeting sessions, which often take place in rural villages. The project has been able to: 1. Visualize existing and/or new government-funded (county and national level) development projects which will enable the counties to know which projects have been completed, which are in progress and the projects that are pending. 2. Perform a needs assessment analysis through the Participatory Budget meetings where the counties engage the citizens and together determine the distributions of the projects. 3. Assist citizens to monitor progress of projects and hold the government accountable for delivering on its promises. 4. Transfer knowledge of mapping in OSM to county government representatives directly, in offices of M&E, GIS, ICT, and Budgeting. Using OpenStreetMap and sharing the data with the counties is a huge milestone for Map Kibera as this will encourage more institutions and people using OpenStreetMap within both communities and county government. This session will share the process and tools being used in the project and early outcomes.

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How I used OSM data to visualize gender disparity in street names for all of Brazil. The result shows how women are underrepresented in street names in the country, and raises questions on who is chosen to be commemorated in street names.

Using OSM road data and a database of gender popularity for names, I created a map visualization to show gender disparity in street names in Brazil. Streets named after women represent only a small proportion of the streets in Brazil. This proportion is even smaller when we consider the length instead of the number of streets. In Brazil, streets are typically named after prominent historical figures, including politicians, business people, military, religious figures, artists, and academics, among others. The small representation of women among these reveals who is chosen to be regarded as prominent, and thus commemorated in a street name, and who isn't. Another interesting aspect is that the map allows up to see certain areas and neighborhoods where female street names predominate. Upon quick visual examination, these appear to be usually in neighborhoods at the periphery of large cities. More in-depth research could indicate if there's a spatial pattern here and if it is related to other social phenomena. The interactive map was based on the Road Orientations Map by Vladimir Agafonkin. I used Mapbox and mbtiles for the map visualization. To process the source data, I used Geofabrik extracts of OSM data and Postgres / PostGIS scripts. The interactive map is here https://medidasp.com/projetos/genero-ruas/#12/-23.5617/-46.6469 and the github repo is here https://github.com/bplmp/genero-ruas-mapa

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Want a postcard? Looking for somebody to send a postcard to? Me too! Let's discuss how people in OpenStreetMap come together, which pleasant and otherwise experiences we had meeting other mappers, and how to express gratitude and make people feel a bit closer to each other — with postcards.

Each time I visit a SotM conference far from home, I'm collecting postal addresses from everyone who follows my news channel. And I send postcards: "Hello from Aizuwakamatsu!" We rely on digital too much: nothing we can touch, nothing we can put on a shelf. Virtual maps, virtual gratitude. With this project, OSM postcrossing, I plan to give every mapper a chance to have something tangible as an outcome of participating in our project. But to get there, we must think of what brings us here, and what experiences we have as members of the community. The premise is simple: ask somebody for their postal address. Except people are reluctant to give it: the address is a private information, and privacy is important. Have you tried to upload a gpx trace, to see how we value it? A lot. So what do you do? Do you send a postcard to a random mapper? Do you publish your address for everyone to see? Should you stay back from the official real-world services and back away to the comfort of virtuality?

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We have seen an explosion of OSM mapping in the last few years around maps for development and humanitarian uses, particularly in Africa. During this time it has also become clear that sustaining this essential mapping work, and keeping maps up to date, was going to be a primary concern. Building a healthy mapping ecosystem around mapping for development will not necessarily be able to follow the same model as it has in more developed countries. In this talk, I will share the culmination of my research on sustainability with the World Bank’s Global Facility for Disaster Reduction and Recovery and their Open Cities Africa project, and some ways that we can best support mappers and grow a healthier global OSM ecosystem.

Two years ago, at SOTM in Milan, I and several colleagues held a panel discussion around sustainability challenges that were common to working with OSM in developing countries. Mappers spoke up about their struggles continuing to map, keeping maps up to date, and growing their mapper communities in places with very few resources. This research has now concluded with a white paper on the topic, and many new learnings along the way. In this talk, I’ll review the outcomes of "Sustainability in OpenStreetMap", https://opendri.org/resource/sustainability-in-openstreetmap/, a publication under OpenDRI at the World Bank. While many groups and projects are arising to do mapping throughout the world, and many for a social purpose, we found a number of challenges to keeping mapping moving forward and overcoming hurdles. These include financial, technological, social, and political challenges, each with its own kind of possible solutions. I also looked into the varieties of OSM actors that would be most likely found in contexts of OSM in development, and what challenges they each may face. These actors include governmental mappers, independent consultants, businesses and startups, nonprofit organizations of a wide variety, international NGOs, formal and informal chapters, and more. Finally, I identified some solutions and supports that are most needed to create a sustainable OSM environment in low-resource geographies. In this talk I will share about all of these findings, and we will discuss the best ways to support a strong international mapper ecosystem.

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Do you get together with other mappers in your town? Would your group benefit from a bit more support? In this talk you will learn about the newly reformed Local Chapters & Communities Working group and our effort to support mapping groups all over the world.

Attend this talk to learn more about the Local Chapters and Communities Working Group (LCCWG). This talk will share our current initiatives as well as invite ideas from participants, and will precede the annual Local Chapters Congress, so hopefully we'll see you at both! Who are the Local Chapters & Communities Working Group? Reformed in November 2019, the LCCWG are a small group of OpenStreetMap enthusiasts and community leaders interested in finding and implementing ways for the Foundation to support the growth of local communities. The LCCWG hopes to facilitate a global exchange of ideas and support among local leaders, and work together to create strong local communities. Right now we have 3 focus areas: building local community cohesion, sharing ideas and best practices globally. We hope to encourage established communities to further organise themselves and eventually formally affiliate with the Foundation as one of its Local Chapters. We will review the role of Local Chapters within the Foundation and the interactions between them. Based on our findings we will make recommendations to the Board as to how the affiliation scheme can be improved to provide a stronger case for local communities to eventually become Local Chapters, or possibly suggest creating new affiliation models such as less-formal user groups. Interested in representing your community on the Working Group? Start by joining the conversation today! Find out more about the LCCWG on the Wiki https://wiki.osmfoundation.org/wiki/Local_Chapters_and_Communities_Working_Group.

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In Cusco, Peru, during the last 2 years, GAL Center worked with students using OSM and associated tools, such as Kobo as educational tools, mainly for research into social problems that the students themselves identify in their locality. Projects such as “Sexist advertising mapping”, “Sexual health” and “Garbage mapping in Larapa” were the result of this work. This year, MapImpact is one of the HOT Microgrants and we will work with high school students and YouthMappers Chapters that we help to create in universities. In this talk, I will tell you more about how we work MapImpact in GAL: our objectives, our methodology, our results and why we would like it to be replicated in other places.

MapImpact is a GAL Center project in Cusco, Peru, which aims to make more students aware of the problems that afflict their locality, to achieve this they will have to investigate these problems using OSM and associated tools such as Kobo Collect, as the main research tool. To develop this project GAL had a previous experience of 2 years, in which we work with high school students in different provinces of Cusco and help in the creation of the first YouthMappers Chapters in Peru, projects such as “Sexist advertising mapping”, “Sexual health” and “Garbage mapping in Larapa” were the result of this work. This year we will work MapImpact also with university students and our intention is for students to understand digital maps not only as a tool to generate geospatial data, but also as a tool of social impact, that is, the process not end when they save the changes in OSM, they have to use that information to make visible a problem that afflicts their location. In this talk we would like to share with you the whole process we follow to reach this point.

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OpenStreetMappers are a diverse group of people. This short segment will introduce you to another person that makes the project what it is.

Enjoy this little break from longer talks and get to know a conference delegate that you can talk to at the conference. As OpenStreetMap is made by us all, it's important to get to know each other and this can form a nice ice-breaker or give you suggestions on conversation starters. Gregory has two unique OpenStreetMappers for us to meet and chat to. One has only been a member of the project for a couple of years, getting involved due to a call from the Humanitarian OpenStreetMap Team(HOT) but also some local projects to map solar panels. The other OSMer has been involved for more years, and has started running out of new things to map in her local area so helps the project in other ways.

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This presentation describes the ongoing work at the Urban Analytics Lab at the National University of Singapore, developing novel methods to assess building completeness at a multi-country scale, as part of a broader project of generating 3D city models on a large-scale using OpenStreetMap.

Quality assessment of OpenStreetMap (OSM) data has been an important topic since the inception of the project. Much research has been done on this topic by many research groups around the world, and it can mostly be seen as permutations of three aspects: (1) spatial data quality element(s) in focus (e.g. positional accuracy, completeness), (2) theme (e.g. amenities, buildings, roads), (3) geographical area (e.g. particular city or country); e.g. positional accuracy of cultural features in Italy. Completeness is one of the principal quality aspects of geospatial data, and our research focuses on developing a method to assess the completeness of buildings in OSM on a large scale (spanning several countries). While there are many robust OpenStreetMap completeness techniques and studies developed, they mainly focus on limited areas, mostly developed countries with ground truth data at hand for comparisons. Doing the same for less developed regions is rare as the lack of authoritative data inherently hampers it, and the methods hardly ever scale: such an analysis done simultaneously for more than one administrative region is seldom carried out as there are other research challenges such as disparate urban morphology, different data sources and standards to bridge in order to facilitate ground truth, and varying understanding of what a building is. Furthermore, the development of a method that would scale across dozens of countries is limited by computational resources. We are currently developing a method that uses several indicators derived from remote sensing, which are available on the global level, that may hint at the building completeness and would scale across the world. A regression model to predict the approximate volume of buildings in a given area is trained in areas in which there is an indication of high completeness of buildings in OSM. OSM building completeness is estimated by comparing the number of mapped buildings against their expected (predicted) amount in reality. The method has the potential to scale at a worldwide level, and completeness is estimated for a grid of resolution of approximately 1x1 square kilometres, and simultaneously for administrative regions to enable cross-country comparisons. The work is being implemented in Google Earth Engine, mostly relying on imagery and indicators such as normalised difference built-up index (NDBI) and normalised difference vegetation index (NDVI). Preliminary results suggest a substantial disparity in OSM building completeness around the world, with areas that are entirely complete to those with inadequate completeness. This presentation aims to report the progress of the ongoing work and encountered challenges in the project such as selecting indicators that are consistently available on the global level, scaling the method to the global level, accounting for different mapping practices around the world, different notions of a building, and varying morphologies of cities and urban areas. We also investigate the relation between OSM building completeness and socio-economic parameters such as GDP to understand their relationships to mapping intensity and quality. These may offer the potential to be used as additional predictors. This work is part of a broader project conducted at the Urban Analytics Lab at the National University of Singapore on investigating the potential of generating 3D city models by extruding building footprints in OpenStreetMap to a building height that is predicted using artificial intelligence. This ongoing portion of the project is the crucial first step in the project, as it will enable us to understand what is the completeness of building footprints in OpenStreetMap around the world and manage expectations about the potential coverage of 3D city models.

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The Philippines is to be considered one of the most-LGBT friendly countries in the World. In 2019, it was able to host the largest pride celebration in Asia. Amidst all this, crimes against LGBTQ+, discrimination, and bullying is still rampant in the country. The Sexual Orientation and Gender Identity Expression (SOGIE) Bill is still continuously being delayed. It is intended to prevent various economic and public accommodation-related acts of discrimination against people based on their sexual orientation, gender identity or expression. Despite of being tolerated, the LGBT community is still far from being accepted by society. Evidence of our community have been written on books, told in stories, presented in movies and yet the community has not left its mark in data. Spreadsheets, research, books have identified spaces where community activities happen but this are not shown on any map online. Our spaces are mere descriptions or addresses on tables and paragraphs. This talk would be about how we would be more represented on OpenStreetMap so as to provide emphasis on being on the map.

MapBeks is an online community of mapping volunteers that advocates for diversity inclusion and representation focused specifically for Lesbians, Gays, Bisexuals, Transgendered, Queer, Inter-sexed, etc. (LGBTQI+) on OpenStreetMap. As part of its advocacy is to map-out and locate all HIV facilities (testing, counselling, and treatment hubs) in the Philippines. It has researched, collated, and validated various sources to build an updated and comprehensive online database with location data. Currently, it has identified 650 HIV testing and counselling centers all over the Philippines and already mapped out 140 (20%) of the facilities on OSM using MapContrib.xyz. We would like to share our experiences in building our small community of LGTQ+ advocates, and digital volunteers. We hope we can inspire the world with our endeavour to make change from what little we have. The talk will discuss the following: 1. The STATUS QUO- LGBT places are not that much represented on OSM/ tagging/ lack of data 2. How was Map Beks able to start up as a local community and how it was able to reach out to the growing LGBT and PLHIV community 3. Its current projects and advocacies 4. Its plans for the future

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Non-government organizations and local government units use geographic data from OpenStreetMap (OSM) to target humanitarian aid and public services. As more people start to depend on OSM, it is important to study data completeness in order to identify unmapped regions so that OSM volunteers can focus their attention on these areas. In this study, we propose a method to measure the data completeness of OSM building footprints using human settlements data.

Non-government organizations and local government units use geographic data from OpenStreetMap (OSM) to target humanitarian aid and public services. As more people start to depend on OSM, it is important to study data completeness in order to identify unmapped regions so that OSM volunteers can focus their attention on these areas. In this study, we propose a method to measure the data completeness of OSM building footprints using human settlements data. Specifically, we use Facebook’s High Resolution Settlement Layer (HRSL), a dataset of built-up areas derived from satellite images, as a proxy for ground truth building footprints. We then measure data completeness by getting the “percentage completeness” of pixels which is computed using the total percentage of pixels within the intersection of the human settlement layer and the OSM building footprints. The method can be broken down into three steps: (1) convert the human settlement layer into a vector; (2) perform a spatial join to find the intersection between the vectorized human settlement layer and the building footprints; and (3) calculate the data completeness based on pixels from the vectorized human settlement layer that intersect with the building footprints. Chepeish and Polchlopek [1] conducted a similar study measuring data completeness in OSM building footprints. We differentiate our work from Chepeish and Polchlopek [1] in three ways. First, for the human settlement layers, Chepeish and Polchlopek [1] used WorldPop which has a spatial resolution of 100 meters [2] while we used the High Resolution Settlement Layer (HRSL) from Facebook which has a spatial resolution of 30 meters [3]. Second, for the data processing, their group rasterized the building footprints while our group vectorized the human settlement layer. Third, for calculating the data completeness, their group used a combination of geographic information system (GIS) and machine learning (ML) while our group solely used GIS. Using building footprints from January 2020 and human settlement layers dated June 2019 and October 2018, the percentage completeness is 32.75% and 10.89% for the Philippines and Madagascar, respectively. We found that in the Philippines, most of the unmapped pixels are in rural areas. When the pixels are aggregated to the municipality-level and plotted as a scatter plot of the urban percentage completeness vs. the rural percentage completeness, the municipalities appear to group together into two categories: sparsely mapped and thoroughly mapped. A possible explanation is that there are not enough OSM volunteers to map all municipalities and the OSM community focuses on thoroughly mapping high population municipalities rather than moderately mapping all municipalities. Interestingly, poverty incidence data from the Philippine Statistics Authority is not correlated with data completeness. Complete or incomplete OSM data in an area is not an indicator of wealth or poverty. As this work has garnered interest from humanitarian organizations such as the Humanitarian OpenStreetMap Team (HOTOSM), to whom regularly updated information on OSM data completeness is extremely valuable, we looked into ways to automate workflows in QGIS by using the built-in workflow builder tool (i.e. Processing Modeller) and by using the QGIS API. However, as we consider scalability and reproducibility important for this line of work, we ultimately deemed QGIS to be unfit for our use case. QGIS is not scalable because the data processing is not easily parallelizable and it is also not easily reproducible by developers who do not have training with GIS software. Thus, we decided to migrate our workflow to GeoPandas and rasterio and open-source our code [4]. Our workflow improved because (1) we were able to speed up the process by migrating to the cloud and increasing the computing resources; and (2) we were able to improve the reproducibility by allowing us to communicate our work more effectively to people who aren’t familiar with GIS. For future research, we recommend exploring other human settlement datasets The Global Human Settlement Layer (GHSL), for example, has a spatial resolution of 30 meters [5] which is comparable to WorldPop and HRSL. We also encourage further data analysis on the percentage completeness in order to get insights on how to improve the process of contributing to OSM. [1] Chepeish, E., Polchlopek, J. (2018), Estimate OSM building coverage completeness by comparing vs WorldPop raster, GitHub repository, https://github.com/azavea/hot-osm-population. [2] Tatem, A (2017). WorldPop, open data for spatial demography. Sci Data 4(1), 170004, doi.org/10.1038/sdata.2017.4 [3] Tiecke, T.G., Liu, X., Zhang, A., Gros, A., Li, N., Yetman, G., Kilic, T., Murray, S., Blankespoor, B., Prydz, E.B., Dang, H.H. (2017), Mapping the world population one building at a time, arXiv:1712.05839. [4] Orden, A., Flores, R.A. (2020), Supplementary code for "Measuring OpenStreetMap building footprint completeness using human settlement layers, GitHub repository, https://github.com/thinkingmachines/osm-completeness.

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The number of OSM contributors every year tends to increase. But not all are sustainable contributors. For example in Indonesia, there is a lot of OSM training and Mapathon but it is suspected that there are not many local contributors of all time. For this reason, extracting information from OSM accounts that have been registered since a few years ago that classified as a rare mapper. The method is by recording the top 500 accounts in Indonesia, identifying local accounts based on profiles and heatmaps, sending questionnaires, and summarizing them. The results can be used by the Indonesian OSM community to increase the sustainability of the contribution of local people in OSM.

In many developing countries, the number of OSM contributors tends to increase every year. But the amount of increase has not been directly proportional to the sustainability of contributions to OSM. Indonesia is an interesting example of developing countries with many OSM agendas from various parties (Grab, HOTOSM, RedCross, IFRC, OSM Indonesia, National Disaster Agency, etc.) including training and Mapathon. In general, OSM was introduced in Indonesia by HOT OSM since 2012. Until 8 years later there were not many local mappers who contributed sustainably. It is also interesting to explore as a basis for increasing sustainable contributions to OSM by local mappers or mappers who have a certain area of ​​interest. Tools from Pascal Neis help to identify the top 500 newest mappers in Indonesia. As well as the user profile page and heatmap to identify the background mapper and their interest areas. After being selected the "local" accounts are then grouped based on their activity level. Then send structured messages through all their OSM accounts. The results of the interviews are then grouped based on the activity of contributing and the age of the account. Then the statistics are determined based on the diversity of answers. The results of this interview are expected to be useful for all stakeholders who directly and indirectly deal with OSM. Concrete steps for an awareness-raising strategy contribute to OSM on an ongoing basis in every OSM event held.

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Contributors of OpenStreetMap data for Mozambique, a country in Southern Africa, were classified into four distinct groups. The most active group included 25% of all contributors, most of them long-term contributors, and most features were last edited by members of this group. One can therefore conclude that the quality of the data is likely to be good, however, it lacks in completeness and the number of edits per feature is low. Even though no absolute statements about data quality can be made, the analysis provides valuable insight into the quality and can inform efforts to further improve the quality.

OpenStreetMap (OSM) has made it possible for any volunteer to contribute geographic information, regardless of their level of experience or skills. Since the task of creating geographic information is no longer exclusively performed by trained professionals, data quality can be a concern. Uncertainty about the quality of data contributed by volunteers has been cited as a hindrance to its use (Mooney and Morgan, 2015). As the number of OSM contributors continues to grow, gaining knowledge about their characteristics and the kind of data they contribute is important. Data quality can be assessed extrinsically, i.e. against reference datasets, or intrinsically, i.e. by analysing the data itself. OSM data quality has often been assessed extrinsically by comparing it to other reference datasets (Girres and Touya, 2010; Haklay, 2010; Neis et al, 2012; Helbich et al, 2012; Mooney and Corcoran, 2012; Fan et al, 2014; Dorn et al, 2015). However, such reference data is not always available and therefore intrinsic assessment methods have been employed (Anderson et al, 2018; Barron et al, 2014). For example, analysing contributors and their contributions can answer questions, such as: What kind of contributors (e.g. experienced vs newcomers) have worked on the data in the area? In which areas should the data be validated or updated (e.g. where data has been contributed by newer contributors or older non-recurring contributors). In this study, contributors and their contributions to OSM in Mozambique, a country in Southern Africa, were analysed in order to gain insight into the quality of the data. We chose Mozambique because in 2019, it received a significant amount of attention in the OSM community following the floods and damages as a result of cyclones Idai and Kenneth. The OSM contributors were characterised in three steps: 1) OSM history data, containing information about the contributors and their contributions, was downloaded; 2) using cluster analysis, OSM contributors were classified according to their contribution characteristics; 3) based on the classification, the OSM data contributors were characterised in Mozambique in order to get insight into the quality of the data. OSM history data provides a record of all the edits (or changes) performed on OSM features. Each edit results in an increment in a feature’s version number. Each version of a feature is associated with a contributor (called ‘user’). The results of the cluster analysis revealed four distinct classes of contributors. The most active class of contributors had 2,552 volunteers (25% of all contributors in the area), with on average the highest numbers of changesets, total contributions, node contributions, way contributions and ways and nodes for which they were the last user to modify them. These volunteers are ‘older’ contributors who have sustained their contributions in the Mozambique area over a long period of time, with the first contributions dating back around 15 years. Compared to the other contributors, they have mapped on more days than the others and the average number of edits per feature is higher. Nevertheless, 99.6% of buildings and 84% of ways in the data had been edited twice at most. Buildings are almost entirely concentrated within the centre of Mozambique, in the areas for which the mapathons were conducted. In some European countries, the number of edits per feature is much higher. Similar to the results of other OSM contribution analyses (Neis and Zipf, 2012), most of the data generated in Mozambique has been contributed by a small group of active contributors who have dedicated a significant amount of time to this. Studies have suggested that such contributors are more likely to be experienced and knowledgeable about the project (Bégin et al, 2013; Budhathoki and Haythornthwaite, 2013; Barron et al, 2014; Yang et al, 2016) and are therefore more likely to produce data that is of good quality (Barron et al, 2014; Yang et al, 2016; Anderson et al, 2018). Even though no absolute statements can be made about the quality of the OSM data for Mozambique, analysing contributors and their contributions provides valuable insight into the quality of the data and can inform efforts to further improve the quality. The results of this study show how one can gain a better understanding of the community that contributes data in a specific area by inspecting history data. Intrinsic methods for evaluating data quality should not seek to replace ground truthing or the use of reference data sets for evaluating data quality (i.e. extrinsic methods), but rather, to complement these methods by providing alternative ways to gain insight about data quality when reference data is not available.

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Plenty of research about behavioural differences between men and women for years ago. According to a scientific article in 2013 by Lewis, on average women may have better verbal memory and social cognition, whereas men may have better motor and spatial skills. Moreover, spatial skill is really needed for mapping, especially as a mapper volunteer in OSM that everyone can make their own map. It also has been known that male mappers more dominate OSM mapping than female mappers. Nevertheless, in some mapper communities, the number of female mappers more than male mappers, for example in Humanitarian OpenStreetMap Team Indonesia. 19 from 30 mappers in HOT Indonesia are female, yet does it affect the performance and quality of mapping in OpenStreetMap?

It is true that men mappers more dominate OpenStreetMap (OSM) mapping than women mappers. Some mapper communities have the number of female mappers than males mappers. For instance in Humanitarian OpenStreetMap Team Indonesia (HOT Indonesia), 19 from 30 mappers in HOT Indonesia are female. Moreover, it is a fact that men and women have some behavioural differences and it might affect their working performance, especially in OSM mapping. According to the number of changesets, addition, deletion and modification in OSM mapping that tracked through OSMCha, it can be seen the difference between female and male mappers when they are mapping some objects in OSM. OSMCha offers many features for reviewing OSM, one of the features is to review a changeset and mapper details that include the mapper username, the number of changesets that mapper has contributed in OSM. Besides, it has changeset-map for visualising of changeset on OSM and the addition, deletion, and modification can be seen by other OSM users. Thus, through those data, it can be seen how is the difference between females and males when they are contributing data in OSM, also how the quality of OSM map that they create. I believe, those differences can be helpful for creating better data for OSM and invite another female to contribute in OSM.

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In an ideal map, every point is reachable to another. However, in OSM data for instance, only 98.59% of Singapore’s nodes are reachable to each other by a path. In this talk, we identify OSM map problems by checking the connectivity of the road network using strongly connected component algorithms and introduce a creative visualisation to help map ops pinpoint the fix effortlessly. Using this approach, we have fixed thousands of map problems in SEA.

In an ideal map, every point is reachable to another. Like any crowd-sourced product, it is a challenging goal for OSM to be ideal because the edits are from contributors with various backgrounds. For instance, only 98.59% of Singapore’s nodes are reachable to each other. This could cause significant problems when routing from one point to another for any business use case. In June 2019, some contributor mistakenly tagged one of the five major expressways in Singapore with Access=No, which subsequently caused all the routing through the expressway to fail. In this talk, we address the issue by using strongly connected component algorithms to identify such map problems and building a creative visualisation to help map analysts pinpoint the fix effortlessly. Using this technique, we identify map errors such as two one-way roads meeting each other with opposite directions; duplicate nodes causing roads disconnected; parking lots not connected to main road network and more. The detected map errors spread everywhere on the map that motivates us to build a creative visualisation to help map analysts pinpoint the erroneous nodes/ways. Using this approach, we have fixed thousands of map problems in SEA.

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The “localness” of data is often described as a major factor for the authenticity of (geo-) information in OpenStreetMap. However, the exact meaning and relevance of “localness” remain controversial. We compare proposals made for the “measurement”, i.e. for the empirical operationalization, of “localness”. Based on this, two convincing operationalizations were selected and implemented in order to contrast regional differences in “localness”. Our analysis allows the identification of regions in which exceptionally high proportions of data are mapped remotely – mostly regions in the Global South. Bearing this in mind, we discuss how “localness” is negotiated in the OSM community.

A frequently reproduced mantra of the debate on Volunteered Geographic Information (VGI) can be summarized by the term ”localness”: It emphasizes the actual or at least desired local production of geographical information. Through new tools in Web 2.0 it is assumed that now a large number of “ordinary” people “on the ground” can generate knowledge about their everyday environment. This postulated “local expertise” operates as a claim to truth of VGI. In contrast to “conventional” geodata, whose truth claim is more likely to be based on professional and technical expertise, VGI is often legitimized by its authenticity due to its “localness” (Goodchild 2007, 220 or Elwood et al. 2012, 584). In fact, relatively little is known about how “local” VGI data actually are. Although “localness” is accepted as a central quality feature of VGI (Barron et al. 2014), it is hardly taken into account in approaches to measuring the quality of VGI (Senaratne et al. 2016, 161). However, we compare existing methods for measuring “localness”, with particular emphasis on the different geographical scales on which they work. Additonally, considering that the importance of “localness” is controversially discussed in the OSM community, further research is conducted in OSM wikis, blogs, group chats, at conferences and with interviews. The key interest is to explore the significance and relevance of “localness”, and simultaneously investigate the implications of an interference of local mappers with remote mappers. The presented research follows a mixed methods approach. Firstly, a synopsis of all methods that have been already used to measure “localness” is implemented. The effort to determine where OSM users have their origin is necessary because this information is not saved within the OSM user data or cannot be read simply from IP addresses (Quinn 2016, 6). In addition, the methods aim to find out where the density of local information is high. Many of the used methods work best at a regional scale, only some function for global data. Since we are most interested in the latter, we have implemented two methods that are possible on a global scale. Using the first OSM changeset of a user, we determine the density of local mappers across the world (Neis 2013). Furthermore, the sum of all used OSM keys or the sum of the different OSM keys in an area per number of OSM elements can be used to identify areas with a higher density of local content. Zielstra et al. (2014, 1227f) have already used this method on a regional level for selected mappers; with Zipf et al. (2019) we are able to calculate this on a global scale. The second part of the study consists of qualitative interviews and analysis of documents such as OSM wikis, blog pages and group chats. After a preliminary research, the aim is to dive into exemplary local communities in which conflicts between local and non-local mappers become evident. For instance, the contested meaning of localness for humanitarian mapping between local map guards and international aid organisations will be further investigated. In addition, the emerging issue of the relationship between maps produced with the aid of “artificial intelligence” and local mapping communities will be explored. Frequently, “localness” is considered an important indicator for the authenticity of data in OSM. The research carried out should therefore answer the question whether locally produced data in OSM are “better” than data not produced locally. Moreover, we also approach conflicts between local and external mappers by examining debates and discussions within the OSM community. This research thus helps to gain a better understanding of the conflicts that exist within OSM and between different user groups within the OSM community. References Barron, C., Neis, P., & Zipf, A. (2014). A Comprehensive Framework for Intrinsic OpenStreetMap Quality Analysis. Transactions in GIS 18(6), 877–895. Goodchild, M.F. (2007). Citizens as sensors. The world of volunteered geography. GeoJournal 69(4), 211–221. Elwood, S.A., Goodchild, M.F., & Sui, D.Z. (2012). Researching volunteered geographic information: Spatial data, geographic research, and new social practice. Annals of the American Association of Geographers 102(3), 571–590. Neis, P. (2013). The OpenStreetMap Contributors Map aka Who’s around me? Available online: http://neis-one.org/2013/01/oooc/ (accessed on 05 March 2020). Quinn, S. (2016). A Geolinguistic Approach for Comprehending Local Influence in OpenStreetMap. Cartographica, 51(2), 67–83. Senaratne, H. et al. (2016). A review of volunteered geographic information quality assessment methods. International Journal of Geographical Information Science 31(1), 139–167. Zielstra, D.W.G. et al. (2014). Areal delineation of home regions from contribution and editing patterns in OpenStreetMap. IJGI 3(4), 1211–1233. Zipf, A. et al. (2019). OpenStreetMap History Data Analytics Platform. Available online: https://heigit.org/big-spatial-data-analytics-en/ohsome/ (accessed on 28 February 2020).

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Recently, OpenStreetMap (OSM) shows great potentials in providing massive and freely accessible training samples to further empower geospatial machine learning activities. We developed a flexible framework to automatically generate customized training samples from historical OSM data, which in the meantime provide the OSM intrinsic quality measurements as an additional feature. Moreover, different satellite imagery APIs and machine learning tasks are supported within the framework.

After more than a decade rapid development of volunteered geographic information (VGI), VGI has already become one of the most important research topics in GIScience community [1]. Almost in the meantime, we have witnessed the ever-fast growth of geospatial machine learning technologies in intelligent GiServices [2] or addressing remote sensing tasks [3], for instance land use land cover classification, object detection, and change detection. Nevertheless, the lack of abundant training samples as well as accurate semantic information has been long identified as a modelling bottleneck of such data-hungry machine learning application. Correspondingly, OpenStreetMap (OSM) shows great potentials in tackling this bottleneck challenge by providing massive and freely accessible geospatial training samples [4]. More importantly, OSM has exclusive access to its full historical data [5], which could be further analyzed and employed to provide intrinsic data quality measurements of the training samples. Therefore, a flexible framework for labeling customized geospatial objects using historical OSM data allows more effective and efficient machine learning. This work approaches the topic of labeling geospatial machine learning samples by providing a flexible framework for automatically customized training samples generation and intrinsic data quality measurement. In more detail, we explored the historical OSM data for twofold purposes of feature extraction and intrinsic assessment. For examples, when training a building detection convolutional neural networks (CNNs), the OSM features with tags as building=residential or building=house are certainly of interests while the data quality of such features might play an important role later in the CNNs training phase. Therefore, besides the acquisition of the user-defined OSM features, we provide additional intrinsic quality measurements. Currently, we consider some basic statistics, such as the areas of buildings tagged with different OSM tags, the amount of distinct contributors in the last six months, or the equisdistance of polygons with landuse=cropland etc , since the existing research suggested that the lower equisdistance of the current polygon, the better relative quality of the polygon, which due to the further refining and editing from users [6]. In the future, one could also easily extend the current framework and develop other sophisticated quality indicators for specific “fitness-for-use” purposes. Heterogeneous remote sensing APIs are supported within the framework, user’s option ranges from commercial satellite image providers (e.g., Bing or Mapbox) to government satellite missions (e.g., Sentinel-hub), even user-defined tile map service (TMS) API. Correspond to OSM features, the satellite image would be automatically downloaded via TMS and tiled into proper size. Moreover, this framework also supports different machine learning tasks, like classification, object detection, and semantic segmentation, which requires distinct sample formats. The preliminary test is performed to extract the geographical information of water dams with OSM tag waterway=dam, which enables the training of water dams detection CNNs, where users could easily change the geospatial water dams to customize objects as long as the corresponding OSM tags are identified. This work aim to promote the application of geospatial machine learning by generating and assessing OSM training samples of user-specified objects, which not only allows user to train geospatial detection models, but also introduce the intrinsic quality assessment into the “black box” of the training of machine learning models. Based on a deeper understanding of training samples quality, future efforts are needed towards more understandable and geographical aware machine learning models. References [1] Yan, Y., Feng, C., Huang, W., Fan, H., Wang, Y. & Zipf, A., (2020) Volunteered geographic information research in the first decade: a narrative review of selected journal articles in GIScience, International Journal of Geographical Information Science. [2] Yue, P., Baumann, P., Bugbee, K. & Jiang, L., (2015). Towards intelligent GIServices. Earth Sci Inform 8, 463–481. [3] Zhu, X., Tuia, D., Mou, L., Xia, G., Zhang, L., Xu, F. & Fraundorfer, F. (2017) Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources IEEE Geoscience and Remote Sensing Magazine, vol. 5, no. 4, pp. 8-36. [4] Li, H., Herfort , B., Zipf , A. (2019 ): Estimating OpenStreetMap Missing Built up Areas using Pre trained Deep Neural Networks. Proceedings of the 22nd AGILE Conference, Cyprus. [5] Raifer, M., Troilo, R., Kowatsch, F., Auer, M., Loos, L., Marx, S., Przybill, K., Fendrich, S., Mocnik, F.-B.& Zipf, A., 2019. OSHDB: a framework for spatio-temporal analysis of OpenStreetMap history data. Open Geospatial Data, Software and Standards 4, 3. [6] Barron, C., Neis, P. & Zipf, A. (2014) A Comprehensive Framework for Intrinsic OpenStreetMap Quality Analysis. Transactions in GIS, 18(6), 877–895.

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The collaboration between the Italian Alpine Club (CAI) and OpenStreetMap (OSM) officially began with the signing of an agreement between CAI and Wikimedia Italia, the Italian chapter of the OSM Foundation, in 2016. The first activity was to define a standard to be used for CAI objects to be mapped using the wiki, and this started the mapping. Three years after that signature much has been done with a surge in the last year also thanks to the funding of CAI through the project "CONTRACT FOR THE DATA IMPLEMENTATION SERVICE IN THE INFOMONT SYSTEM". This financed one person to carry out different activities: - the data entry in OSM with the procedure used and the situation region by region - the development of software released under a FOSS license able to obtain CAI data from OSM and carry out some conversion and reporting operations - training activities in the different sections of the CAI During the presentation will be made a history of the activities of the CAI with OSM, the results obtained so far and the various features of the software developed.

The collaboration between the Italian Alpine Club (CAI) and OpenStreetMap (OSM) officially began with the signing of an agreement between CAI and Wikimedia Italia, the Italian chapter of the OSM Foundation, in 2016. The first activity was to define a standard to be used for CAI objects to be mapped using the wiki, and this started the mapping. Three years after that signature much has been done with a surge in the last year also thanks to the funding of CAI through the project "CONTRACT FOR THE DATA IMPLEMENTATION SERVICE IN THE INFOMONT SYSTEM". This financed one person to carry out different activities: - the data entry in OSM with the procedure used and the situation region by region - the development of software released under a FOSS license able to obtain CAI data from OSM and carry out some conversion and reporting operations - training activities in the different sections of the CAI During the presentation will be made a history of the activities of the CAI with OSM, the results obtained so far and the various features of the software developed.

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Trufi Association NGO offers an open-source journey planner app for formal and informal transport, based on public transport mapped in OpenStreetMap. In this extended talk, I would like to explain, how the participants can be customize the app to their own city, region, and country.

With Trufi, mappers and developers can customize an open-source journey planner app for their own city, region or country. Especially in emergent countries in Africa, Middle and South America and Southeast Asia, where informal traffic (no stops, undocumented routes) is mainly in use. For the public transport data part, OSM is used to map the routes, OSM2GTFS or our own tools are used to create GTFS, which is hosted in OTP. Besides the obvious tasks (map routes, customize app code), many more important steps need to be done: Get a team together, find develop perfect UX for your city, release, finance the server and map costs, do marketing, convince governments to collaborate, approach users, run analysis on data, improve OpenStreetMap routes, offer solutions for drivers, etc. I would like to talk about these necessary steps, with stories from our implementations in Bolivia, Ghana, Ethiopia, and Colombia. Finally, I would like to discuss in an extended Q&A part whether OpenStreetMap is made for 100s of (bus) routes - we had fruitful discussions with pros and cons on that in the talk-de mailing list in July 2019 that we could continue.

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The study contributes towards some best practices of carrying out community mapping exercise and, distribution of results freely on OSM and spatial data portal like MASDAP for further studies or decision making. Thus the study focused on preparing for mapping - what to map, how to map and how to record the data; the mapping exercise itself; downloading and digitizing of data in map production; and how to use the maps to aid in decision making.

The greatest threat to the people, property and economy of Malawi are natural hazards. According to Misomali (2014), since 1946, of all the 298 times the country has been impacted by hazards, 89% of those have been natural hazards while the remaining 11% have been human-generated events. According to MacOpiyo, records indicate that in the last 100 years, the country has experienced about 20 droughts in the last 100 years while in the last 36 years alone, the country has experienced eight major droughts, which have affected over 24 million people (MacOpiyo, 2017). The problem that exists almost every year in Malawi is that it is hit by floods in quite a number of districts. Households, infrastructures: roads, schools, praying houses are affected much alike like cultivation fields. These disasters impact negatively on the social-economic growth of the communities involved and the country at large to some extent. For example MacOpiyo pointed out that in 2015 Malawi experienced a once-in-500-years flood which impacted more than 1.1 million people (MacOpiyo, 2017). Therefore the study aimed at collecting exposure data which was used for production of flood risk maps. These maps were in turn used in Atlas production. Primary and secondary data was used in this study. Secondary data came from disaster profile from the Department of Disaster and Management Affairs (DoDMA) in Malawi which helped in identification of flood prone areas. Secondary data which was exposure data such as buildings, toilets, roads, bridges and schools among others was collected using handheld GPS with an accuracy of 3m. Choice of this data was based on how it impacts on the social-economic activities of the concerned communities. Java OpenStreet Map (JOSM) software was used for digitizing the collected exposure data by overlaying it with satellite imagery and creating attributes of that data. Thereafter, this was uploaded into a GIS environment for conducting symbolization and map visualization. Various maps at different scales were produced which showed location of different areas that were affected by floods. These maps are used to inform the affected communities on areas that are prone to floods. The maps are shared on OpenStreeMap, Malawi Spatial Data Portal (MASDAP) and at regional and district level through workshops. Thus communities should know where to settle or not as well as where they can carry out their socio-economic activities and not be affected by flood. This also helps in building resilience among communities. The study has shown that there is little interventions on the ground to help in reducing the root cause of floods in most parts of the country. There are a lot of mixed reasons as to the causes of floods that affect people in these areas. Living in low lying areas is the main attributed that was found for communities to be heavily affected by floods. Another reason is the siltation within rivers which is causing the rising-up of river beds is another cause of flooding in the country. These have heavily affected the poorly constructed buildings and infrastructures found in the flood prone areas. Another problem that was observed during the course of this study is lack of knowledge and information on disaster risk management. This has resulted in communities being affected when flood disaster strikes. As a way forward, the study proposes that the government and other stakeholder must equip communities with long term interventions in building their capacities and resilience to reduce vulnerabilities. This might be in form of building of dykes along river banks that floods; enforcement of proper construction standards when putting up infrastructures and that communities must also strive to build permanent dwelling houses with strong foundations.

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To assess the possible human and financial losses of earthquakes and to estimate the long-term earthquake risk that many people on Earth are exposed to, detailed knowledge of buildings is paramount. This encompasses not only the position, size, and type of buildings, but also the reconstruction value and the number of people inside the building at any time. Using OpenStreetMap data and further open data, we are implementing an open, global, dynamic, purely algorithmic, and reproducible exposure model for the probabilistic description of the aforementioned parameters for every building on Earth, growing and changing with every edit in OpenStreetMap.

Earthquakes are threatening many regions in the world with constantly increasing risk due to rapid urbanization and industrialization. To improve resilience and preparedness, we need to estimate the risk of earthquakes with the greatest possible detail. For this, exposure models are used that translate the physical earthquake hazard to building damage, human and financial losses. The level of detail in the risk model directly depends on the level of detail in the exposure model. So far, exposure models are usually described as aggregated building-type descriptions for larger geographic areas, from city districts to even larger administrative units. We present our new open, dynamic, and global exposure model based on OpenStreetMap that does not stop at administrative boundaries but rather attempts to classify and describe every building on Earth with the greatest level of detail. Our open-source model extracts all possible exposure indicators, i.e. footprint shape, number of stories, occupancy type, shape of the roof, etc. and combines the OpenStreetMap data with other open datasets if available. Using these indicators, the model assesses in a probabilistic way the possible building classes, the number of people inside the building depending on the time and day, and the reconstruction value. In areas with incomplete building coverage, the classical aggregate-based exposure models are combined with our model to deliver a probabilistic description of the entire building stock. To achieve a better spatial distribution of buildings in areas of incomplete coverage, we estimate the likely locations of buildings through remote sensing using again open data only, mainly Sentinel-I radar data. Due to the near-realtime computations of our model, it directly profits from the growth of OpenStreetMap and with about 5 million buildings added each month, the areas of incomplete coverage are constantly shrinking, making way for our building-specific exposure model.

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This study explores the following research questions using OpenStreetMap-based mapping approach and healthcare facility survey from one of seven slums being studied in Africa and Asia. What are the differentials of spatial proximity to health care providers in informal settlements like slum? What are some of the lessons learnt from using OpenStreetMap-based mapping approach for slum health research? Preliminary findings suggest that residents can access four categories of healthcare facilities (Clinics/Maternity Centres; Patent Medicine Stores; Traditional/Faith Healers; Eye Health Centre) within a walking distance (under 1km) where Clinics and Maternity Centres are farthest from most residents.

Background. The United Nation Sustainable Development Goal (SDG) 3 seeks to ensure universal health coverage for everyone irrespective of geographical location by 2030. Anecdotal evidence exists on the possibility of attainment of the goal at household level in slum areas most especially in Africa. Recent studies suggest further work on the advancement of empirical evidence on slum health [1], [2]; especially in Africa where slum population growth is reported to be at the same level with urban population growth [3]. There is the need to understand the dimensions of spatial proximity to healthcare facilities in Nigeria towards achieving SDG 3 [4]; especially in slum areas where little evidence exists. Spatial access to appropriate healthcare is even more relevant given the rapid rise in Covid-19 cases globally. The lack of detailed quality spatial data is a concern to both researchers and development agencies [5]. In an attempt to contribute to the knowledge gap in slum health studies, this study draws on two data sets (field validated OpenStreetMap data and healthcare facility survey data) from an ongoing research project to examine spatial proximity to healthcare facilities (HCFs) in Sasa, an informal urban “slum” area in Nigeria. The decision to focus on spatial proximity is based on findings from a household survey, in an ongoing project, which suggest that one of the main reasons given by respondents for choosing HCFs is proximity. Conceptually, there are two main schools of thought about spatial proximity [6]; this study considers proximity as a distance measure defined quantitatively. The ongoing research project is a National Institute for Health Research (NIHR) Global Health Unit on Improving Health in Slums at University of Warwick [7]. This Unit focuses on health services in slums through the study of seven slum sites in Africa and Asia and aims at finding optimal ways to enhance health services. We thus present initial results from one of the study sites in Africa. The following research questions are explored. What are the differentials of spatial proximity to health care providers in informal settlements like slum? What are some of the lessons learnt from using OpenStreetMap-based mapping approach for slum health research? Method. An OpenStreetMap-based data collection methodological approach was developed and implemented [8]. A spatially-referenced sampling frame was generated through a combination of: remote participatory mapping from satellite imagery; local participatory mapping and ground-truthing; and the identification of dwellings of each validated structure. Additionally, a healthcare facility survey was conducted to capture types of facilities etc. The following categories of HCFs are drawn from survey data and used for analyses: four Clinics and Maternity Centres (CMC); twenty-two Patent Medicine Stores (PMS); five Traditional and Faith Healers (TFH); and, one Eye Health Centre (EHC). Two-fold analyses are conducted. First, two measures of spatial proximity (spatial network and Euclidean distances) to different types of HCFs within the site are computed using field validated OpenStreetMap (OSM) network data. Bivariate analysis is performed to test sinuosity (ratio of network and Euclidean distance). Additionally, comparative analyses of combined means and medians (using k-independent samples median tests) for categories of HCFs are performed. Second, a reflective exercise is undertaken to outline some of the lessons learnt during the research process related to the OSM-based approach. Result. The presentation will discuss the outcome of the two-fold inquiry outlined. Preliminary results show strong positive correlation (r=.97; 99% CI) between the two spatial proximity measures suggesting that Euclidean and network spaces are quite similar in terms of accessibility to health care services within Sasa slum. Overall sinuosity index is 1.16 suggesting that the non-linear nature of network routes to HCFs contributes to 16% more than the Euclidean metric. The combined network distance grand mean (with standard deviations) and grand medians for each of the categories are as follows: 727m (±299) and 766m for CMC; 579m (±256) and 563m for PMS; 589m (±240) and 589m for TFH; and, 503m (±204) and 490m for EHC. Residents can access these facilities within a walking distance (under 1km) where Clinics and Maternity Centres appear to be the farthest from most residents. This study advances the evidence base on slum health towards achieving SDG 3 and promotes the use of OSM-based mapping approach and data for slum health research.

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Most field program managers have their go-to field preparation checklist - this often includes a data model, their preferred data collection tools, field survey timeline, to name a few. We are often cautioned about the importance of community entry, and it is right, you will not be able to just enter the community and start mapping as people will get curious, ask questions and possibly become suspicious or hesitant to accept your data collection activities. At HOT, we employe participatory mapping methods and encourage local people to map their communities. However, sometimes with even all the correct steps followed, your activities can be hindered due to factors outside of your control. In this session, we will explore one of HOT’s field mapping projects implemented in Kampala in collaboration with the Kampala Capital City Authority that aimed to map community-level flood risk in a local suburb along the Nakamiro Channel catchment area. Despite all the correct steps taken, community entry in a specific jurisdiction felt impossible and field mapping could not be carried out. In this session, our aim is to first discuss what went wrong and how our field team approached this situation and later invite participants/attendees to share similar challenges experienced in the field and how these situations were overcome or addressed.

Most field program managers have their go-to field preparation checklist - this often includes a data model, their preferred data collection tools, field survey timeline, to name a few. We are often cautioned about the importance of community entry, and it is right, you will not be able to just enter the community and start mapping as people will get curious, ask questions and possibly become suspicious or hesitant to accept your data collection activities. At HOT, we employe participatory mapping methods and encourage local people to map their communities. However, sometimes with even all the correct steps followed, your activities can be hindered due to factors outside of your control. In this session, we will explore one of HOT’s field mapping projects implemented in Kampala in collaboration with the Kampala Capital City Authority that aimed to map community-level flood risk in a local suburb along the Nakamiro Channel catchment area. Despite all the correct steps taken, community entry in a specific jurisdiction felt impossible and field mapping could not be carried out. In this session, our aim is to first discuss what went wrong and how our field team approached this situation and later invite participants/attendees to share similar challenges experienced in the field and how these situations were overcome or addressed.

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Since 2010 organized humanitarian mapping has evolved as a constant and growing element of the global OpenStreetMap (OSM) community. We analyse the history of humanitarian mapping using OpenStreetMap History and OSM Tasking Manager (tasks.hotosm.org) data. We conduct a comprehensive quantitative analysis on a global scale and long term perspective to depict more than just snapshots of individual events. Results show that in regard to edits, users, projects, geographic diversity, almost all of these have experienced linear growth. But regarding user commitment and validation efforts we conclude that the humanitarian mapping community still faces huge challenges to achieve sustainability.

## Introduction Since 2010 organized humanitarian mapping has evolved as a constant and growing element of the global OpenStreetMap (OSM) community. In the last few years, several researchers have analyzed humanitarian mapping practices (e.g. Zook et al. 2010, Soden et al. 2016, Dittus et al. 2017), however most of this work was either event-driven or focused on specific time periods and regions. The OSM ecosystem and the actors involved in it are constantly changing and emerging, for instance also due to the mapping activities of corporates (Anderson et al. 2019). ## Purpose of this study In our work we analyse the history of almost 10 years of humanitarian mapping in OpenStreetMap using the OSM Tasking Manager (tasks.hotosm.org). We conduct a comprehensive quantitative analysis on a global scale and long term perspective in order to depict more than just snapshots of individual events. We show how humanitarian mapping was impacted by major mapping disaster response events, but also widened its application to other domains such as disaster preparedness. Our approach follows two paths. One focuses on the mapping itself. The other focuses on the composition of the humanitarian mapping community. We provide insights on the following research questions: 1. Where, for what purpose and at what scale did humanitarian remote mapping actually happen in 2012-2020? 2. What is the impact of humanitarian contributions and how does it differ among user groups and over time? ## Methodology and Results ### Data Our analytical approach is characterized by the combination of the following data sets: (1) For general OSM mapping and user characteristics we use the whole history of OSM object versions and edits provided by the OSHDB framework (Raifer et al. 2019). (2) We were provided with a HOT Tasking Manager database dump covering the time between 11/2012 until 12/2019. ### Method Our method uses spatio-temporal time series analysis based on a hexagonal grid for all areas humanitarian mapping activity has been conducted. We investigate the evolution of humanitarian mapping with respect to several attributes including number of OSM contributions (created, modified, deleted OSM objects), percent of tasks mapped and validated in the Tasking Manager and number of OSM contributors involved. We set our findings and the geographical distribution of our results in context to the overall OSM mapping and user characteristics. ## Results Humanitarian mapping has grown almost linear with respect to the numbers of projects, unique users, tasks mapped and OpenStreetMap contributions. While at the same time it got more diverse in terms of spatial distribution of mapping activities and organizations involved. In 2018 more than 100 organizations were running projects on every inhabited continent except Australia. Peaks in mapping activities in the first quarter of 2015 and the third quarter of 2017 are reflecting activations of the community in responses to the Gorkha earthquake as well as to the hurricanes Harvey, Irma and Puebla earthquake. Although the number of mappers is growing, the humanitarian community lacks long term commitment. Almost 70% of humanitarian mappers contribute one day only. Another 10% drops out after ten days of being active. However the number of one day only contributors is steadily rising, whereas mappers involved on 5 or more days seem to be saturated since 2017. Nonetheless, the one day only contributors have a considerable impact on the data. They account for 10% of all edits, whereas contributors involved on 100 or more days account for 50%. This pattern is reflected by the composition of user actions as well. The number of mapping activities is growing almost linear, whereas the amount of validators stagnate. However, different organisations have varying degrees of success in recruiting new validators. The american red cross for instance accounts for 30 to 50% of all first time validators in the last 12 months. ## Discussion Humanitarian mapping with the tasking manager now exists for more than seven years. In terms of scale it is definitely a success story. We have shown edits, users, projects, geographic diversity, almost all of these have experienced linear growth. But with respect to the numbers of user commitment and validation efforts we conclude that the humanitarian mapping community still faces huge challenges to achieve sustainability. Only a small proportion of users contribute regularly and an even smaller fraction does validation. But this very phenomenon is nothing new for OSM in general. Nevertheless, we have seen that some organisations and communities are more successful than others in recruiting and retaining users. Our results may support decisions for future strategies on user engagement. However to what degree this community composition affects data quality remains open. Within our work our definition of humanitarian mapping is rather narrowed. Our insights about humanitarian mapping in OSM provide only an incomplete picture which lacks an on-the-ground perspective and neglects other remote mapping tools.

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OSM is more fun and useful with quicker access to fresh data. New web services, tools and file formats enable mappers to download and use edited data within minutes.

Workflows for adding data to OpenStreetMap keep getting better - but once an edit is uploaded, it can take a long time to appear on hosted map tiles or regional OSM extracts. The experience of downloading and using OpenStreetMap can be improved by quick access to fresh data - a fast feedback loop makes contributing to OSM more enjoyable! The first part of this talk covers a new web service, Minutely Extracts (protomaps.com/extracts). Mappers can download rectangular or polygon regions of the world, with changes replicated every minute from planet.openstreetmap.org. I’ll show some example workflows for using Minutely Extracts with common GIS applications such as QGIS, and new tools for conversion to formats like GeoPackage and Shapefile. The second part of this talk describes OSM Express (osmx), a new spatially indexed file format powering Minutely Extracts, that supports in-place updates. Developers needing random access to OSM objects can consider embedding osmx as a library. Near real-time editing activity visualization is one possible use case. I’ll review the technical tradeoffs between using osmx, PBFs and other popular formats. Financial Background: The software is developed as open source, under a BSD license, and funded by the author's commercial OSM-based SaaS services as well as OSM contract software development.

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We outline methods for a) extracting the geometry of street blocks in urban centres using OSM and remote sensing data, b) generating approximate cadastral maps of a block given contained building footprints, and c) quantifying residents’ ability to navigate within blocks through topological analysis of cadastral maps. This topological metric, termed “spatial accessibility” and denoted k = 1, 2, 3, ..., determines whether areas of a city are informal settlements, as blocks where k > 2 contain cadastral parcels without direct access to formal road networks. We analysed 1 terabyte of OpenStreetMap data for 120 low and middle income (LMIC) countries.

Determinations of whether a neighbourhood is underserviced depends greatly on local concerns, however topological analysis of building footprints in relation streetlevel access offers a pathway to near-universal criteria for determining whether a neighbourhood is potentially a slum or urban informal settlement. With a topological approach, it is possible to determine the number of building parcels a streetblock inhabitant must cross to access a potential services bearing street, i.e. one that enables for example access to emergency services, sanitation and piped water. Focusing on topological invariants allows us to analyse cities without respect to the specific morphology of their street network [1]. To create a global index of these under-serviced neighbourhoods, we extracted open-source data on building footprints and street networks and applied a topological analysis to each extracted street block to characterise the level of spatial accessibility. Using these techniques, we analysed 1 terabyte of OpenStreetMap (OSM) data to create an index of street block geometry, cadastral maps, and spatial accessibility calculations for 120 Low and Middle Income Countries (LIMCs) in the Global South. These results highlight the developing world’s most spatially inaccessible communities, enabling prioritisation of infrastructure investment and further study. The primary data universe for our work involves three entities: 1) administrative boundary polygons, 2) building footprint polygons, and 3) street network line-string collections. The administrative data boundaries were sourced from the Database of Global Administrative Areas, while building footprints and street networks were sourced from OSM. Since OSM does not provide direct downloads, the data under consideration was obtained from a mirror of OSM hosted by GeoFabrik. This data reflects a snapshot of OSM as of July 2019. Starting with the initial road network, the geometry of the street network can be extracted via the well-studied process of polygonization. In polygonization, the self-intersections of the street network are determined in order to define the block geometry. Most GIS software packages offer a polygonization feature (e.g. ST_MakePolygon from PostGIS). In implementation, however, we found a set-theoretic approach to be more stable and performant [2]. By buffering the one-dimensional line-strings comprising the street network with a small buffer radius, we obtain two-dimensional polygons capturing the outline of the street network. We then find the set-theoretic difference between the administrative boundary polygons and the buffered road-network polygon; this renders the negative area between the road network geometry as a collection of polygons. These negative areas are precisely the street blocks we are trying to extract. With each building’s access to the formal road network - or lack thereof - analysed, we can also provide suggestions to the local community about ways to connect each building to the existing road network while respecting existing building footprints. This process, known as re-blocking, is in general, NP-hard. In contrast to previous algorithmic re-blocking efforts involving stochastic graph search [3], we approach the problem using Steiner tree approximations to the optimal road network. To frame the universal access network as a Steiner tree problem, we segmented the building parcel boundaries to create “non-terminal” nodes at each segment boundary, and create a “terminal” node for each building parcel lacking direct street access by placing a node on the non-street parcel boundary closest to the building. We then construct a complete subgraph of all the terminal nodes, where the weight of each edge is the Euclidean distance between each pair of terminal nodes; each existing road segment has zero weight. Solving the minimum-spanning tree problem on this subgraph, and then recovering the original path segments, gives us the optimal new road network. In the short future, we hope to repeat our analysis on denser sources of data on footprints and street network data from other providers (such as private satellite imagery companies). We expect that while OpenStreetMap has good coverage in some areas, we can use data in which footprints are automatically extracted from satellite imagery to supplement areas where coverage may be lacking. Additionally, we hope to re-block more neighbourhoods and cities and investigate the connections between an urban centre’s infrastructural topology and the amount of new infrastructure needed to be built to universally connect each building to the formal road network. Finally, we expect that our generated dataset could be used to study development and health outcomes, especially around questions of tenure security and emergency service provision.

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Today, nearly 17% of the global road network was last edited by a corporate data-team member. We further investigate unique editing patterns among three corporations that have specific, localized impacts on the map.

**Introduction** OpenStreetMap (OSM), the largest crowdsourced geographic database has garnered interest from corporations over the last four years. Today, major corporations including Apple, Facebook, and Microsoft have dedicated teams contributing to OSM. More than 2,300 OSM editors are associated with corporate data teams, up from approximately 1,000 in 2019 [1]. As of March 2020, nearly 17% of the global road network (measured per kilometer) was most recently edited by a corporate data-team member. Each corporation edits according to their own agenda; displaying unique patterns of edits with respect to types of features edited, mode (manual, import, or machine assisted), and locations and volume of edits. **Aim** We investigate the unique editing patterns associated with three corporations: Grab, Digital Egypt, and Tesla, the latter two’s editing activity has never previously been quantified. Differing from other corporations with high volumes of global editing [1], these corporations exhibit uniquely specific patterns. **Methodology** We use a combination of OSM data processing pipelines including tile-reduce, osm-qa-tiles, and osm-interaction tilesets [2] to extract and quantify the edits associated with the corporations. **Results and Discussion** **Grab** is a Singapore-based company active in South-East Asia offering ride-hailing transport food delivery, and payment services. Grab is actively editing OSM data since 2018 and has thus far edited 1.6M features. Grab’s focus on transport related services implies that a navigable road network is a priority. However, topology and navigation restrictions are difficult to encode. Grab dedicates efforts to improve road navigability. In Singapore, Grab has edited over 100,000 turn restrictions, comprising 95% of all turn restrictions in Singapore (and 7% of all turn restrictions globally). This represents a highly focused effort put in by a corporation in a specific place to build infrastructure needed to support their business. Overall, Grab’s efforts of improving data and building a community of editors in South-East Asia is beneficial for the OSM ecosystem. **Digital Egypt (DE)** aims to produce detailed and accurate GIS and Mapping data in Egypt, Middle East and Africa. Active in a part of the world with sparse geographic data coverage, Digital Egypt’s team of 24 mappers works to improve the accuracy of OSM, specifically as it relates to improving address information for improved geocoding in Egypt [3]. As of March 2020, DE has edited more than 2M features, more than 1.7M of these edits involve objects with address tags (e.g. addr:housenumber). These edits comprise 94% of the objects in Egypt that have an address tag. Similar to Grab, DE’s contributions improve the usability of OSM data for everyone. Unlike Grab, DE does not operate services built on top of this data infrastructure. Their hyperlocal focus and dedication to improving data for a country with a dearth of spatial data distinguishes them from other corporations using map data in a product. **Tesla**—an American manufacturer of electric cars—was revealed to be using OSM parking aisle information for their vehicle’s self-driving “summon” feature in a blog post in November 2019 [4]. To function properly, the summon feature requires detailed maps of parking aisles within larger parking lots. These data were relatively sparse, as their utility to the overall map is minimal compared to the actual road network. However, the number of parking aisles added to OSM in North America has increased by approximately 71% in 2018-2020 from an average of 322 ways per day in 2016-2018. While there is no official Tesla data-team that is mapping these features, in the days following the blog post, the number of new OSM editors adding parking aisles within their first day of editing jumped from an average of 1.5 to 10. A single blog post inspired dozens of Tesla fans to join OSM and add new parking aisles to the map, thereby mapping a specific feature type for a narrow purpose. These Tesla owners represent a new generation of hobby and “craft mappers” in OSM. Unlike traditional ‘craft-mappers’ considered to altruistically contribute map data about specific features, the Tesla mappers create data intended to be consumed by a corporation to enhance the experience for a select group of motorists. If looking to categorize these new mappers into prior community labels, their mapping practices have more in common with a “traditional craft-mapper” than a paid, corporate mapper. Grab, Digital Egypt, and Tesla represent three distinct cases of corporations consuming, contributing, and driving further interest in OSM. **References** https://docs.google.com/document/d/183aN5Ph2KGuR6iBE_i8j-mL8h2saH3EYU65WMrZWNsw/edit?usp=sharing

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Lightning Talk session

## [Divide and map. Now.](https://wiki.openstreetmap.org/wiki/Divide_and_map._Now.) *Jiri Vlasak* - [Website](https://www.damn-project.org/), [Slides](https://qeef.gitlab.io/talks/damn-lightning-talk/) The damn project helps mappers by dividing some big area into smaller squares that a human can map. ## Mapping Historically Black Colleges and Universities in OSM *Harrison Cole* Historically black colleges and universities (HBCUs) are schools that were established exclusively for Black Americans prior to 1964. As of June 19th, 2020, their campuses are disproportionately undermapped in OSM relative to institutions with predominately white student populations. After putting out the call to map these campuses, volunteers have come together to submit thousands of edits and ensure that the campuses are represented properly. In this talk, I give a brief overview of how far the effort has come, and what still needs to be done. ## DDD123-OSM: 2D and 3D render toolchain *Jose J. Montes* Introducing the toolset we are building to generate 3D models from OpenStreetMap data, used in our under-development "racing in your own city" game, and sharing some pictures and videos. ## OSGeo + OSM *Enock Seth Nyamador* - [Slides](https://enockseth.github.io/sotm-2020-osgeo-plus-osm-lightning-talk-enyamador/) Quick introduction to Open Source Geospatial Foundation (OSGeo) and highlighting projects for working with OSM Data. ## Tasking Manager 4 Tour *Wille Marcel* The new version of Tasking Manager, released on April 2020, has a new completely redesigned interface. This talk is a guided tour on the main parts of Tasking Manager 4. ## Uses of Mapping for Community Care During the Pandemic *Andi Tabinas* How were we able to use maps for community care during the COVID-19 situation in the Philippines? Through this lightning talk, I would like to share about how our volunteer organization, Mental Health AWHEREness, has been using maps and OSM data for community care in the Philippines. ## OpenStreetMap Data Pacific *John Bryant* Interest in open geospatial is growing in Pacific Island countries, where budgets and data availability are big problems, but the OpenStreetMap community in the region is still small. This project aims to introduce Pacific "geo" people to OSM by providing a weekly extract of 14 countries in a GIS-friendly format, bundled with a QGIS project and enhanced with some basic cartography. ## Hikar.js - bringing Hikar to the web *Nick Whitelegg* Hikar is an augmented reality app which overlays OSM ways on the camera feed of an Android device, and was presented last year at SOTM Heidelberg. The past year has seen a surge of interest in the three.js and A-Frame based augmented reality library AR.js, which allows AR apps to be developed for the web. This lightning talk summarises efforts so far to port Hikar to the web using AR.js. ## [Mapathon Keralam](https://wiki.openstreetmap.org/wiki/Kerala_State_IT_Mission) *Amitha K Biju* Mapathon Keralam is being coordinated by the Kerala State IT Mission, with the concept of Let Us Make Our Map.

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Walkability is the foundation for urban life that is sustainable, inclusive, healthy, and dignified. Pedestrians First is a new open-source suite of tools for using OSM data to measure indicators of urban walkability. During this talk, we will examine the nature of walkable and unwalkable cities, we will discuss the opportunities and limitations of using OSM to measure walkability, and we will consider possible avenues for extending Pedestrians First in the future.

Few modern cities are walkable. Around the globe, cities have been designed for cars rather than pedestrians, resulting in enormous costs to the environment, the economy, and even the social fabric of our communities. In the face of the climate crisis, there is an urgent need to build cities that are sustainable and inclusive. Responding to that need, the Institute for Transportation and Development Policy has released Pedestrians First, a tool that supports planners and decision-makers by using OSM to measure what matters for walkability in cities. This talk will describe what makes a city walkable, discuss the experience of using OSM to measure walkability, and explore possibilities for improving that measurement in the future. Because the experience of walking is so natural, it is easy to assume that planning walkable cities is equally simple. That is far from the case. To be walkable, a city must prioritize pedestrians not only in urban design but also in land use and transportation planning. Some factors, like sidewalk quality, matter at the small scale, the level of the street or the city block. Other factors matter at the level of the neighborhood, such as the proximity of destinations, and still others are at the level of the entire city, like the ability of a public transit system to move pedestrians from one neighborhood to another. We have found OpenStreetMap an invaluable tool in identifying and measuring the most important elements of walkability. Certain aspects of the OSM data model have been critical to the success of the project, especially its emphasis on representing connectivity, its typology of streets, and its relative uniformity around the world. However, other aspects have been challenging to work with, and prevent us from measuring other important indicators of pedestrian-friendliness. For example, it is still difficult to clearly represent the presence and quality of sidewalks in OSM and assessing the safety of street crossings remains a pressing concern. There are many opportunities to expand Pedestrians First. We are exploring possibilities that include computer vision to identify street assets, integration of traffic data, and measurement of multimodal access to opportunities. We hope to join conversations about how best to implement such approaches within the OpenStreetMap community. With offices in Brazil, China, India, Indonesia, Kenya, Mexico, and the United States, the Institute for Transportation and Development Policy has advocated for sustainable urban transport since 1985. A non-governmental organization striving for inclusive cities, ITDP is dedicated to openness of information and techniques.

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A summary of trademark law basics and an explanation of the OSMF Trademark Policy and how it applies.

The purpose of this talk is to give those interested a foundational explanation of what trademark law is and how the OSMF Trademark Policy works. There have been several questions raised to LWG regarding the OSMF Trademark Policy and how it applies in certain circumstances. Trademark law is often conflated with other types of intellectual property law and the scope of trademark rights is not well-understood. In addition, the LWG sometimes fields questions from OSM users or groups on how to use the Trademark Policy and forms and templates mentioned in the policy. This talk will go over some common questions and give the audience a chance to ask more. Outline for the talk: Trademarks 101: What is a trademark? (vs other forms of intellectual property, like copyright, data rights, patent, trade secrets, publicity rights) How do you get trademark rights? - Trademarks are jurisdictional - Acquiring rights via usage - Trademark registration Trademark use vs not trademark use - Using a term "as a mark" - Sponsorship/endorsement - What trademark doesn't protect - fair uses OSM trademarks: - The OSMF Trademark Policy - Common uses - Pre-policy use cases - Domains - Using the permission template Questions!

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Surprise, surprise!

Surprise, surprise!

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Maps!

Maps!