chrimerss/flood-foundation-resnet152-unet

ResNet-152 U-Net for Flood Risk Segmentation

Model Description

This model is a ResNet-152 U-Net fine-tuned for flood risk segmentation from Digital Elevation Models (DEM) and precipitation data.

ResNet-152 backbone U-Net for flood segmentation

Model Details

• Architecture: ResNet-152 U-Net
• Training Epochs: 49
• Model Size: 959MB
• Task: Semantic Segmentation (Flood Risk Prediction)
• Input: DEM + Precipitation data
• Output: Flood depth categories

Usage

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-resnet152-unet,
  title={Flood Risk Foundation Model: ResNet-152 U-Net},
  author={FloodRisk-DL Team},
  year={2024},
  url={https://huggingface.co/chrimerss/flood-foundation-resnet152-unet}
}