Mapping from satellite imagery in highly populated areas can be very challenging when dense buildings and trees occlude the surface. Device location signals can provide evidence about the existence of roads in these scenarios. Grab, the leading superapp for deliveries, mobility and financial services in Southeast Asia, collects and processes GPS signals from their network of drivers. These signals are anonymized and averaged to produce vector data after some machine learning inference and centerline extraction. Based on our collaboration with Grab, we present a framework on conflating this data against OSM roads. As a result we identified 240,000 kms of new roads in 62 cities where image-based ML models have difficulty due to occlusion. The outputs are being made public through the Daylight Release.
What's new with the RapiD Editor
RapiD is an advanced OpenStreetMap editor that makes it simple to work with openly licensed geodata and AI-detected features. In this talk we share our progress on bringing new datasets into RapiD,...
