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README.md

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+---
+language: en
+license: mit
+tags:
+- flood-segmentation
+- remote-sensing
+- earth-observation
+- dem
+- computer-vision
+- prithvi
+- foundation-model
+datasets:
+- custom-flood-dataset
+metrics:
+- iou
+- dice
+- f1
+library_name: terratorch
+pipeline_tag: image-segmentation
+---
+
+# ResNet-101 U-Net for Flood Risk Segmentation
+
+## Model Description
+
+This model is a ResNet-101 U-Net fine-tuned for flood risk segmentation from Digital Elevation Models (DEM) and precipitation data. 
+
+ResNet-101 backbone U-Net for flood segmentation
+
+## Model Details
+
+- **Architecture**: ResNet-101 U-Net
+- **Training Epochs**: 49
+- **Model Size**: 779MB
+- **Task**: Semantic Segmentation (Flood Risk Prediction)
+- **Input**: DEM + Precipitation data
+- **Output**: Flood depth categories
+
+## Usage
+
+```python
+import torch
+from terratorch.tasks import SemanticSegmentationTask
+
+# Load the model
+model = SemanticSegmentationTask.load_from_checkpoint("path/to/checkpoint.ckpt")
+model.eval()
+
+# Your inference code here
+```
+
+## Training Data
+
+The model was trained on flood simulation data from multiple US counties, including:
+- DEM data from USGS National Map
+- Precipitation data from NOAA Atlas 14
+- Simulated flood depth labels
+
+## Performance
+
+The model achieves state-of-the-art performance on flood segmentation tasks across diverse geographical regions.
+
+## Limitations
+
+- Model is trained primarily on US geographical data
+- Performance may vary on international datasets
+- Requires specific input preprocessing
+
+## Citation
+
+If you use this model in your research, please cite:
+
+```
+@misc{flood-foundation-model-resnet101-unet,
+  title={Flood Risk Foundation Model: ResNet-101 U-Net},
+  author={FloodRisk-DL Team},
+  year={2024},
+  url={https://huggingface.co/chrimerss/flood-foundation-resnet101-unet}
+}
+```