chrimerss/flood-foundation-prithvi-600m

Prithvi-600M for Flood Risk Segmentation

Model Description

This model is a Prithvi-600M fine-tuned for flood risk segmentation from Digital Elevation Models (DEM) and precipitation data.

Prithvi-600M foundation model fine-tuned for flood segmentation

Model Details

• —Architecture: Prithvi-600M
• —Training Epochs: 15
• —Model Size: 6.2GB
• —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-prithvi-600m,
  title={Flood Risk Foundation Model: Prithvi-600M},
  author={FloodRisk-DL Team},
  year={2024},
  url={https://huggingface.co/chrimerss/flood-foundation-prithvi-600m}
}