I am curious where did the Uber get the 3d map of the world.
Edit: It's mentioned at the footnote of the article: "constructed from publicly available aerial LiDAR data". That poses an additional problem - Aerial lidar data globally is very hard to come by - it does not exist for most places in the world and it's also out of date. Shameless plug: At tensorflight.com we built deep learning models for 2.5D map of buildings based on satellite imagery.
The don't need it for the world, just rich industrialised cities, this makes the amount of data much lower. Also, I'd guess "publicly available" doesn't mean public domain, but available for purchase by the public or (and its common in developed cities) generated by the government (for flooding, mapping uses etc)
Furthermore, the article does not mention a 3d model of the world from Lidar data either - it specifically mentions just San Francisco
It's also easy for them to make a very specific, narrow list of locations where the data is needed.
Just look at the raw GPS measurements and look for signs of trouble (high uncertainty, coordinates "teleporting" users around unnaturally, etc.). Maybe you only need it on certain particular streets within a city.
There are commercial LIDAR drone services you can hire, so worst case they just pay some of those people to give them 3D models for the specific areas where they have the worst problems and the highest traffic, and they get a huge improvement.
There are a few internal sources Uber could be getting its mapping data from[1][2]
These acquisitions came around the time Uber was pivoting into a logistics company. It wouldn't be too surprising if they had a pretty rich 3d map dataset of a few test cities. Especially if they had deployed autonomous cars in them.
Google has 3D imagery available for a whole bunch of cities on Google Maps. (Though I don't think they have an API for that data yet.) For places where 3D data isn't available, you can always fall back to regular GPS.
Google is also using 2D imagery to generate 3d structures in rural areas, as demonstrated in this fascinating essay[1]. Details like roof shape, building height, and even bay windows are included.
Edit: It's mentioned at the footnote of the article: "constructed from publicly available aerial LiDAR data". That poses an additional problem - Aerial lidar data globally is very hard to come by - it does not exist for most places in the world and it's also out of date. Shameless plug: At tensorflight.com we built deep learning models for 2.5D map of buildings based on satellite imagery.