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davidclara/
siegfried-maps-segmentation

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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 at ETH Zurich (Spring 2026). Source code: 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.