Update README.md

README.md

@@ -5,6 +5,34 @@ tags:
 - image-segmentation
 - historical-maps
 - cartography
+- vectorization
 library_name: pytorch
 ---
 
+
+# Building Block Vectorization in Historical Maps
+
+Polygon extraction of building blocks from historical maps of Paris. Trained as part of the course [Research Topics in Cartography](https://karto.ethz.ch/en/education/courses/master/research-topics-cartography.html) at ETH Zurich (Spring 2026). Source code: [github.com/dav1dclara/cartography-research](https://github.com/dav1dclara/cartography-research).
+
+## Models
+
+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.
+
+| Subfolder | Architecture | Encoder | Folds |
+|---|---|---|---|
+| `unet_scse` | U-Net + SCSE attention | EfficientNet-B3 | 2 |
+
+## Usage
+
+```bash
+pip install torch segmentation-models-pytorch safetensors huggingface_hub pillow numpy
+
+python inference.py \
+  --hf-repo davidclara/building-block-vectorization \
+  --model-name unet_scse \
+  --image map.jpg \
+  --out mask.png
+```
+
+`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](https://github.com/dav1dclara/cartography-research).
+