2019-09-21 –, Hörsaal Ost
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.
imagery, mapillary, ai, computer vision
Christopher Beddow is a Solutions Engineer for Mapillary. Originally from Montana, USA, he frequently travels while collecting Mapillary imagery, editing OSM, and working remotely on business and community solutions.
I'm the Strategic Partnerships Manager at Mapillary. I work with the community and partners contributing imagery to the platform. Admittedly I first discovered OpenStreetMap when I joined Mapillary in 2015, but I quickly went from "Why would people spend their personal time on this" to "This is an incredible project that I want to be part of". I try to help out in both a work capacity and a personal capacity, arranging mapathons in my local Melbourne, working on FOSS4G SotM Oceania 2018 + 2019, editing in OpenStreetMap, and of course collecting as much street-level imagery as I can.
I love to travel as well and have started to discover the thrill of meeting OpenStreetMap communities in different parts of the world. If you're ever in Melbourne, come and say hi!