trailbehind/DeepOSM

Running the code is as easy as install Docker, make dev, and run a script. Open an issue if you want to discuss something to do, or email me. By default, DeepOSM will analyze about 200 sq. For training data, DeepOSM cuts tiles out of NAIP images, which provide 1-meter-per-pixel resolution, with RGB infrared data bands. For training labels, DeepOSM uses PBF extracts of OSM data, which contain features/ways in binary format that can be munged with Python.