Mapillary is on the mission to help fix the world’s maps by making map updates available at scale.
The old way of collecting data and editing maps manually is too time-consuming to keep up with how fast cities and roads are changing.
Shortly put, we use computer vision to detect objects in images and reconstruct places in 3D.
By combining the two, we can estimate the coordinates of each object, and make that data available as map features.
This means fresh data at scale for a lot of use cases across various industries, such as: And many more—you can see the full list of supported object classes here.
Altogether, we’ve extracted more than 186 million objects as map features across the 430 million images that have been contributed to the Mapillary platform from all over the world.
EDIT: After publishing this post, I realized that I had forgotten about the incredibly helpful Map.update/4 which can make updating maps which have 1 layer of nesting easy. I’ll show you a little talked about and documented way of updating a dynamic nested map in Elixir.
Ranging from utility poles and streetlights to mailboxes and manholes, this will help cities, mapping companies, and transportation agencies keep their maps up to date using cameras.As mentioned, the new map features are currently released in beta.Your feedback will help us prioritize future developments.Learn more about subscriptions and data downloads in our Help Center.With map features, the quality of the outcome depends on both the technology as well as the input—that is, the captured imagery.