Update README.md

README.md

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 # Semantic Segmentation in Historical Maps
 
-Pixel-level land cover classification of historical maps from the Swiss Siegfried map series. 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).
+Pixel-level land cover classification of historical maps from the Swiss Siegfried map series. 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
+
+Seven classes, multi-label (a pixel can belong to multiple classes): **River, Forest, Lake, Wetland, Stream, Building, Road**. Each subfolder contains a `model.safetensors` plus a `config.json` carrying model configuration, class names, per-class decision thresholds, and recommended sliding-window patch size (512).
+
+| Subfolder | Architecture | Encoder |
+|---|---|---|
+| `unet` | U-Net | EfficientNet-B4 |
+| `unet_scse` | U-Net + SCSE attention | EfficientNet-B4 |
+| `unetpp` | U-Net++ | EfficientNet-B4 |
+| `deeplabv3p` | DeepLabV3+ | EfficientNet-B4 |
+| `fpn` | FPN | EfficientNet-B4 |
+| `pan` | PAN | EfficientNet-B4 |
+
+## Usage
+
+```bash
+pip install torch segmentation-models-pytorch safetensors huggingface_hub pillow numpy
+
+python inference.py \
+  --hf-repo davidclara/siegfried-maps-segmentation \
+  --model-name unetpp \
+  --image map.png \
+  --out-dir predictions/
+```
+
+`inference.py` is a minimal example: sliding-window prediction with `stride = patch_size // 2`, ImageNet normalization, sigmoid + per-class thresholding, writing one binary PNG per class to `--out-dir`. For the full training and inference pipeline see the [GitHub repository](https://github.com/dav1dclara/cartography-research).