license: apache-2.0
pipeline_tag: image-segmentation
tags:
- image-segmentation
- historical-maps
- cartography
library_name: pytorch
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
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.