aleguillou1/UNet4RSImage
Notebook to perfom yourself a land cover classification from remote sensing image with a U-Net Model
This project helps environmental scientists, urban planners, and GIS specialists automatically classify different types of land cover from satellite images. You provide a satellite image (like from Pléiades or Sentinel-2) and a corresponding label image defining areas like roads, buildings, or forests. The tool then processes these inputs to give you a map that accurately identifies land cover across your area of interest.
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Use this if you need to perform detailed land cover classification from remote sensing images and want to train a U-Net model with your own specific datasets and labels.
Not ideal if you are looking for a completely automated, zero-setup solution or if you don't have access to GIS software like QGIS for initial data preparation.
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Language
Jupyter Notebook
License
MIT
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Last pushed
Apr 18, 2025
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