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A Sentinel-2 Benchmark and Deep-Learning Study for Wildfire Damage Mapping [Experiment]

A Sentinel-2 Benchmark and Deep-Learning Study for Wildfire Damage Mapping [Experiment]

Valeria Martin, K. Brent Venable Derek Morgan
Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems, pp.304-307
ACM Conferences
SIGSPATIAL '25: 33rd ACM International Conference on Advances in Geographic Information Systems ( Minneapolis, Minnesota, USA, 11/03/2025–11/05/2025)
11/03/2025
: https://doi.org/10.1145/3748636.3762737
: WOS:001672917500028
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Wildfires are increasing in frequency and severity due to climate change, creating a need for accurate, large-scale damage assessment. In this work, we describe a new multi-temporal benchmark dataset for wildfire segmentation using Sentinel-2 imagery and wildfire polygon data, which we call CalFireSeg-50. For 50 of the largest recent wildfires in California (2019 to 2023), we collect RGB, NBR, and NDVI images. Ground truth masks are, then, generated using a conditional approach that combines spatial fire boundaries with burn-sensitive vegetation indices. Using images from this benchmark, we evaluate four deep learning (DL) segmentation models for detecting wildfire damage: UNet, UNet++, TransUNet, and Seg-Former. Our analysis compares model performance across multiple established quality metrics (e.g., IoU and Dice), training time, and data efficiency using a consistent GPU setup. TransUNet achieves the best performance across most quality metrics, including the highest Dice score (88%) and accuracy (94%), while UNet++ obtains the top IoU score (82%). However, TransUNet requires significantly more time to train than the CNN-based approaches. SegFormer shows promise in few-shot settings, but is outperformed by the other models when training involves larger datasets. These findings provide novel insights into the strengths and limitations of CNN, hybrid, and transformer-based architectures for wildfire segmentation, advancing DL approaches for wildfire damage mapping.
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