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levir-cd

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Where did buildings appear between two satellite images? Attention U-Net (PyTorch) vs Random Forest on LEVIR-CD image pairs: test F1 0.59 vs 0.32. The trained model is then applied to Sentinel-2 RGB images of the 24 OSCD cities. Kaggle notebook, built with Kshitij Saxena.

  • Updated Sep 19, 2026
  • Jupyter Notebook

Can frozen DINOv3 satellite embeddings find new buildings? Temporal transformer on SpaceNet-7 and LEVIR-CD, free Colab T4. Test F1 0.91 on LEVIR-CD, where a 0.56M-parameter difference head matched a cross-attention head 1.5x its size; 0.18 on SpaceNet-7's much harder monthly task, against 0.02 for raw embedding distance.

  • Updated Sep 19, 2026
  • Jupyter Notebook

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