Polygon extraction of building blocks from historical maps of Paris. Trained as part of the course Research Topics in Cartography at ETH Zurich (Spring 2026). Source code: github.com/dav1dclara/cartography-research.

Single-class binary segmentation of building blocks ("block"). Each subfolder contains one model_f{N}.safetensors per cross-validation fold plus a shared config.json carrying model configuration, class names, per-fold and ensemble decision thresholds, normalization mode, recommended sliding-window patch size (512), and number of folds.

inference.py is a minimal example: Gaussian-weighted sliding-window prediction with stride = patch_size // 2, normalization driven by config.json (simple = /255, imagenet = mean/std), sigmoid averaged across folds per patch, thresholding with the ensemble threshold, and writing a single binary PNG mask. For the full training and inference pipeline see the GitHub repository.