Edoardo Neerhut

OpenStreetMap is a platform that remains open and accessible, allowing individuals to use, edit, and actively participate in its development. You're invited to join us for a conversation with our local OpenStreetMap leaders, as they explore the delicate balance between structured approaches and the organic nature of grassroots mapping.

Our panellists will sit down and discuss OSM and Oceania. More details to come. This panel will be facilitated by Ed Neerhut.

There is a good chance AR will become a major computing platform. What kind of maps will we need and what is the role of open data in building them? This talk will explore those questions.

Use cases should drive product development, not the other way around. Maps and the products we use to consume them have the biggest impact on the world when these principles are adhered to. How many government portals have you visited where a carefully curated map is presented that hardly anyone sees let alone uses? Presenting the data to the user in an intuitive way that helps them make a decision or take action is essential. Large paper maps of the 1700s were well suited to a captain’s desk as their ships traversed the oceans. Road atlases of the 20th century helped to spur family adventures and weekend getaways as highway networks were constructed around the world. The small computers in our pockets today allow us to see when the next train will arrive and which one will get us home sooner. These examples took the technology of the day and used it to make products with significant impact on society. The mobile internet in particular changed mapping in one of the most notable ways since humans started abstracting 3D space on 2D surfaces. We’re on the cusp of another great shift in the way maps are used with many exciting use cases awaiting discovery. The technology powering this potential is Augmented Reality (AR). This talk will explore some of the use cases that AR is supporting and where it might be useful in future. We’ll look at how AR can be accessed and how the medium of access affects its utility. With these use cases in mind, we’ll assess how open tools and map data enable AR. Some of the data and tools we’ll look at include: * Geometries of pedestrian ways * Associated attributes: Incline, safety, lighting, access, surface type, accessibility features * Building entrances * 3D building data * VPS for localisation * Routing algorithms The talk will conclude with a summary of Meta’s approach to map building and how open source geospatial technology powers the maps we build for today and the years ahead.

Use cases should drive product development, not the other way around. Maps and the products we use to consume them have the biggest impact on the world when these principles are adhered to. How many government portals have you visited where a carefully curated map is presented that hardly anyone sees let alone uses? Presenting the data to the user in an intuitive way that helps them make a decision or take action is essential. Large paper maps of the 1700s were well suited to a captain’s desk as their ships traversed the oceans. Road atlases of the 20th century helped to spur family adventures and weekend getaways as highway networks were constructed around the world. The small computers in our pockets today allow us to see when the next train will arrive and which one will get us home sooner. These examples took the technology of the day and used it to make products with significant impact on society. The mobile internet in particular changed mapping in one of the most notable ways since humans started abstracting 3D space on 2D surfaces. We’re on the cusp of another great shift in the way maps are used with many exciting use cases awaiting discovery. The technology powering this potential is Augmented Reality (AR). This talk will explore some of the use cases that AR is supporting and where it might be useful in future. We’ll look at how AR can be accessed and how the medium of access affects its utility. With these use cases in mind, we’ll assess how open tools and map data enable AR. Some of the data and tools we’ll look at include: Geometries of pedestrian ways Associated attributes: Incline, safety, lighting, access, surface type, accessibility features Building entrances 3D building data VPS for localisation Routing algorithms The talk will conclude with a summary of Meta’s approach to map building and how open source geospatial technology powers the maps we build for today and the years ahead.

None

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.

None

Other people: Christopher Beddow

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

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

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: Christopher Beddow