robosat
An end-to-end Python pipeline for semantic segmentation of aerial and satellite imagery to extract features like buildings and roads.
What is robosat?
RoboSat is an open-source pipeline for semantic segmentation on aerial and satellite imagery. It extracts features such as buildings, parking lots, roads, and water by training neural networks on geo-referenced tile data. The project provides end-to-end tools for data preparation, model training, and post-processing to generate vector geometries.
Geospatial developers, data scientists, and mapping professionals working on automated feature extraction from satellite or aerial imagery. It is particularly useful for OpenStreetMap contributors and organizations needing scalable mapping pipelines.
RoboSat offers a complete, extensible pipeline that abstracts away geospatial complexities with Slippy Map tiles. It integrates seamlessly with OpenStreetMap and supports custom data sources, making it a flexible alternative to proprietary geospatial AI services.
Overview
Semantic segmentation on aerial and satellite imagery. Extracts features such as: buildings, parking lots, roads, water, clouds
Use Cases
Best For
- Automating building footprint extraction for OpenStreetMap
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