nshaud/DeepNetsForEO
Deep networks for Earth Observation
This project helps urban planners, cartographers, and environmental scientists automatically classify and map objects in high-resolution aerial and satellite images. It takes raw remote sensing data, including multispectral images and digital surface models, and outputs detailed semantic maps that highlight different land cover types like buildings, roads, or vegetation. The primary users are researchers and practitioners in Earth Observation who need to analyze large datasets efficiently.
483 stars. No commits in the last 6 months.
Use this if you need to automatically generate detailed land cover maps from high-resolution urban aerial or satellite imagery for research purposes.
Not ideal if you require commercial use, need to analyze other types of remote sensing data beyond urban areas, or are looking for a complete end-to-end mapping solution without deep learning knowledge.
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Last pushed
Oct 23, 2019
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