Christopher Beddow

Mapillary is an open platform for street-level imagery and map data that began in 2013. Since then around 1.8 billion images have been contributed from around the world. Imagery has been contributed from horseback in Kyrgyzstan, boats in the canals of Amsterdam, and bicycles on the streets and trails of Sydney. As Mapillary approaches 2 billion images, we’d like to summarize the latest features, acknowledge some of the amazing contributions, and hint at some of the updates that are coming. Some of the things that we have been working on include: Desktop Uploader improvements including support for videos and popular cameras. Improvements to Mapillary Tools, command line scripts for working with and uploading geotagged imagery and video. Mobile app updates including multi-tasking, redesigns, multi-language support, and upload improvements. Camera Grant programs in the US and Europe, providing 360º cameras for people interested to map pedestrian infrastructure. Integrations with Rapid Editor, an AI powered OpenStreetMap editor which we will demo in more detail at a workshop. Updated Help Pages to make capturing, uploading, and using street-level imagery far easier. After walking through the latest Mapillary improvements, we will take a look at case studies of organizations contributing and using imagery. We’ll zoom in on an NGO, a government agency, and a commercial entity, each of which are using Mapillary in different ways. We’ll finish our talk with an exploration of upcoming Mapillary features and projects. We encourage questions and suggestions in the Q&A and hope for a productive conversation at the end as we walk together towards 2 billion images.

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Other people: Edoardo Neerhut

RapiD is the OpenStreetMap editor that enables users to access a wide variety of shared open data and a machine learning generated predictions that can bring new detail to the map. In 2022, the team at Meta upgraded RapiD to enable users to validate and verify Mapillary detections from images and add data to OpenStreetMap. Join us in an instructional workshop and mapathon where we will learn how Youthmappers in Sierra Leone are using Mapillaryto map the power grid across the country, and how the same workflow can be applied to map electricity availability and lighting around the world, anywhere users capture Mapillary and utilize RapiD to enrich OpenStreetMap.

Goals: - Introduce the project for utility pole/power line mapping in Sierra Leone - demo how to capture Mapillary imagery - walkthrough on how to use RapiD for adding utility poles and street lamps - guided mapathon with focus on Sierra Leone and option to replicate anywhere the participant wants, such as their hometown Logistics: - projector screen - classroom environment good for lecture + laptop work - bring your own laptop + mobile phone - access to wifi needed (we can try to hotspot via my phone) - we will try to provide a few laptops, but if the venue can provide any it will be helpful for using OSM iD/RapiD

Other people: Patricia Solis

RapiD is the OpenStreetMap editor that enables users to access a wide variety of shared open data and a machine learning generated predictions that can bring new detail to the map. In 2022, the team at Meta upgraded RapiD to enable users to validate and verify Mapillary detections from images and add data to OpenStreetMap. Join us in an instructional workshop and mapathon where we will learn how Youthmappers in Sierra Leone are using Mapillaryto map the power grid across the country, and how the same workflow can be applied to map electricity availability and lighting around the world, anywhere users capture Mapillary and utilize RapiD to enrich OpenStreetMap.

Goals: - Introduce the project for utility pole/power line mapping in Sierra Leone - demo how to capture Mapillary imagery - walkthrough on how to use RapiD for adding utility poles and street lamps - guided mapathon with focus on Sierra Leone and option to replicate anywhere the participant wants, such as their hometown Logistics: - projector screen - classroom environment good for lecture + laptop work - bring your own laptop + mobile phone - access to wifi needed (we can try to hotspot via my phone) - we will try to provide a few laptops, but if the venue can provide any it will be helpful for using OSM iD/RapiD

Other people: Patricia Solis

Christopher Beddow takes us on a journey of how to map a small town using a variety of tools. The goal was to test and prove how much OpenStreetMap can be enriched using RapiD buildings and roads, and Mapillary map features, traffic signs, and imagery. He evaluates a small town in the western United States that is far from any mapping community and has very little data, and demonstrates how a vivid dataset can be added to OSM. In addition, he compares this to a small town in Switzerland, demonstrating how new details can still be added to a place that is heavily mapped by a strong local community.

Mapping is a time consuming task, and challenging for an individual to do without a community or a team. However, many tools exist to enhance the capabilities of any lone map contributor, and also multiply the mapping power of a team. Christopher Beddow takes us on a journey of how to map a small town using a variety of tools. The goal was to test and prove how much OpenStreetMap can be enriched using RapiD buildings and roads, and Mapillary map features, traffic signs, and imagery. He evaluates a small town in the western United States that is far from any mapping community and has very little data, and demonstrates how a vivid dataset can be added to OSM. In addition, he compares this to a small town in Switzerland, demonstrating how new details can still be added to a place that is heavily mapped by a strong local community. Leaving this talk, you will understand how to use RapiD, Mapillary, and your own creativity to adapt your own locality into a comprehensive part of OpenStreetMap.

Lightning Talks

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

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

## Deriviste - Click and Go Photo Mapping Christopher Beddow

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

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

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

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

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

## Reverse Geocoding Is Not Simple Ilya Zverev

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

Other people: Ilya Zverev Arnalie Vicario Andi Tabinas Minh Nguyễn Marco Minghini

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

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

Other people: Edoardo Neerhut