TechnicalGIS

#Semantic segmentation

2 posts

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

Six 128 by 128 pixel Sentinel-2 image chips of Nottingham, each shown above the same chip with its label mask overlaid: a city centre with red buildings and grey streets, a suburb, a forest, farmland, flooded gravel pits in blue and an industrial estate.

Tutorial Python Part 1 of 3

Training data for deep learning on satellite imagery: Sentinel-2 image chips in Python

Build a labelled deep learning dataset from free data: stream a Sentinel-2 L2A composite from the Planetary Computer, rasterise Overture building footprints over ESA WorldCover, cut georeferenced image chips, split them by spatial block so the test set does not leak, check the class balance and load it all as a TorchGeo dataset.

Intermediate 13 min read