#segmentation_models_pytorch

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Four rows of Sentinel-2 test chips. Each row shows the image, the reference labels, the U-Net prediction and the disagreement in magenta. The prediction matches rivers, woods and fields closely, but in the city centre it draws buildings as blobs instead of separate roofs.

Tutorial Python Part 2 of 3

U-Net satellite image segmentation in PyTorch: a TorchGeo and Sentinel-2 tutorial on a CPU

Train a U-Net to segment Sentinel-2 imagery into buildings, built-up land, trees, grass and water with PyTorch, segmentation_models_pytorch and TorchGeo's Sentinel-2 pretrained ResNet-18 weights, on a CPU in five minutes. Real loss curves, per-class IoU and F1, a confusion matrix and an honest look at what limits accuracy.

Advanced 12 min read 19 October 2026