souvikmajumder26/Land-Cover-Semantic-Segmentation-PyTorch
🛣 Building an end-to-end Promptable Semantic Segmentation (Computer Vision) project from training to inferencing a model on LandCover.ai data (Satellite Imagery).
This project helps urban planners, environmental analysts, or GIS specialists automatically identify and map different land features from satellite imagery. You input satellite images, and it outputs segmented maps highlighting specific land cover types like buildings, woodlands, or water bodies. This allows users to quickly generate accurate land use classifications for large areas.
187 stars. No commits in the last 6 months.
Use this if you need to accurately categorize and visualize different land cover types from aerial or satellite images for environmental monitoring, urban planning, or geographic analysis.
Not ideal if you need to identify objects within images at a very granular level, such as individual cars or specific tree species, rather than broad land cover categories.
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Jupyter Notebook
License
MIT
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Last pushed
May 15, 2025
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