Semantic segmentation on aerial and satellite imagery

https://github.com/mapbox/robosat

Semantic segmentation on aerial and satellite imagery. Extracts features such as: buildings, parking lots, roads, water

RoboSat is an end-to-end pipeline written in Python 3 for feature extraction from aerial and satellite imagery. Features can be anything visually distinguishable in the imagery for example: buildings, parking lots, roads, or cars.
Have a look at

• this OpenStreetMap diary post where we first introduced RoboSat and show some results.
• this OpenStreetMap diary post where we extract building footprints based on drone imagery in Tanzania.

The tools RoboSat comes with can be categorized as follows:

• data preparation: creating a dataset for training feature extraction models
• training and modeling: segmentation models for feature extraction in images
• post-processing: turning segmentation results into cleaned and simple geometries

Tools work with the Slippy Map tile format to abstract away geo-referenced imagery behind tiles of the same size.