---
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}
}
```