U-Net Pretrained Model
The model was trained on over 3,000 image-mask pairs using Sentinel-2 imagery and CAL FIRE’s Wildland Fire Threat layer, specifically from the 2017 Lilac Fire in San Diego County, California. The training data was curated from the Pala Mesa region, an area significantly impacted by wildfire activity and containing critical road and rail networks.
The model outputs multi-class segmentation masks that classify areas into low-moderate, high, very high, and extreme wildfire threat categories which may support emergency preparedness and response and infrastructure risk analysis.
This pretrained version allows users to test the model and generate inferences without requiring a long runtime for training, making it ideal for rapid evaluation, demonstration, or integration into spatial AI workflows.
Note
- This model was trained on a region-specific dataset (Lilac Fire, 2017), and generalization to other fires or regions may require fine-tuning.
- Masks are aligned with CAL FIRE’s threat classification and were processed using 1,000-foot buffers around infrastructure features.
Data provided by:
- California Department of Forestry and Fire Protection (CAL FIRE)
- European Space Agency (Sentinel-2 via Copernicus Program)
- U.S. Census Bureau (TIGER/Line Roads)
- Federal Railroad Administration / Bureau of Transportation Statistics
