aleguillou1/SemanticSeg4EO
A unified PyTorch framework for semantic segmentation of satellite imagery. Supports multi-spectral data, state-of-the-art architectures, and seamless large-scale inference for Earth Observation applications.
This tool helps Earth Observation specialists automatically identify and map features within satellite and aerial imagery. You input multi-spectral satellite images and their corresponding truth masks, and it produces detailed, pixel-level segmentation maps. It's designed for professionals like GIS analysts, environmental scientists, or urban planners who need to classify land cover, detect changes, or map infrastructure.
Use this if you need to precisely classify large areas of satellite or aerial imagery into distinct categories (e.g., forests, water, buildings) and require robust, artifact-free results.
Not ideal if you're looking for a simple click-and-run solution without any need for data preparation or if you are not comfortable with command-line tools.
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Language
Python
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Last pushed
Feb 25, 2026
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