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deforestation-detection

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gurshh-rain / Multi-Temporal-Land-Cover-Classification-and-Deforestation-Detection-via-Convolutional-Neural-Networ

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Trains a CNN on EuroSAT satellite imagery for land-cover classification (93.6% test accuracy), then applies it to real GeoTIFF scenes across two time points to automatically flag Forest → non-forest transitions as candidate deforestation events.

computer-vision pytorch satellite-imagery cnn-model deforestation-model cnn-architecture land-cover-classification eurosat-satellite deforestation-detection
  • Updated Jul 16, 2026
  • Jupyter Notebook

ZentaCros / convnext-edl-deforestation-amazon

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Fourier Domain Adaptation + Evidential Deep Learning for source-free cross-domain deforestation detection in the Brazilian Amazon using Landsat-8 satellite imagery. ConvNeXt encoder (9.5M params) with per-pixel uncertainty estimation. PyTorch implementation.

computer-vision deep-learning brazil pytorch landsat remote-sensing satellite-imagery uncertainty-estimation semantic-segmentation environmental-monitoring earth-observation domain-adaptation change-detection amazon-rainforest unsupervised-domain-adaptation evidential-deep-learning deforestation-detection fourier-domain-adaptation convnext-land-use-land-cover
  • Updated Jul 22, 2026
  • Python

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