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@@ -13,13 +13,13 @@ In correspondence with the code we released on [GitHub](https://github.com/), th
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  This folder contains the pretrained models, including:
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  - **ResNet18 Classification Backbone**: Pretrained on ImageNet (For more details, see the [PyTorch GitHub repository](https://github.com/pytorch/vision/tree/main/references/classification#resnet)) .
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- - **Binary Road Segmentation Model**: Initialized with the ImageNet classification backbone and trained using cascaded training with Swiss Map data.
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  ## Binary_road_segmentation
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- This folder contains the final model weights used for extracting roads from the Siegfried Map.
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  ## Road_classification_ensemble
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- This folder contains all the model weights for the final road classification ensemble.
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  ## Citation
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  If you find our work useful or interesting, or if you use our code, please cite our paper as follows:
 
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  This folder contains the pretrained models, including:
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  - **ResNet18 Classification Backbone**: Pretrained on ImageNet (For more details, see the [PyTorch GitHub repository](https://github.com/pytorch/vision/tree/main/references/classification#resnet)) .
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+ - **Binary Road Segmentation Model**: Initialized with the ImageNet classification backbone and trained using cascaded training with [Swiss Map](https://www.swisstopo.admin.ch/en/national-map-1-25000) data.
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  ## Binary_road_segmentation
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+ This folder contains the final model weights used for extracting roads from the [Siegfried Map](https://www.swisstopo.admin.ch/en/digital-siegfried-map-1-25000).
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  ## Road_classification_ensemble
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+ This folder contains all the model weights for the final road classification ensemble trained on the [Siegfried Map](https://www.swisstopo.admin.ch/en/digital-siegfried-map-1-25000).
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  ## Citation
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  If you find our work useful or interesting, or if you use our code, please cite our paper as follows: