spaceml-org/STARCOP
Official code for STARCOP: Semantic Segmentation of Methane Plumes with Hyperspectral Machine Learning models :rainbow::artificial_satellite:
This project helps environmental scientists and climate researchers identify methane plumes from satellite and aerial imagery. It takes hyperspectral or multispectral images from sources like AVIRIS-NG, EMIT, or WorldView-3 and outputs precise maps highlighting methane leaks. Professionals focused on climate monitoring, emissions reduction, or environmental impact assessment would use this tool.
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Use this if you need to accurately detect and map methane plumes in remote sensing data, especially from hyperspectral sensors, with reduced false positives compared to traditional methods.
Not ideal if your primary need is identifying methane sources from ground-based sensors or if you only work with general-purpose RGB aerial photography without specialized spectral bands.
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GPL-3.0
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Jul 24, 2025
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