ScholarWorks@성균관대학교

초록

Wildfire damage detection has a pivotal role in forest management and recovery strategy planning. This study demonstrates that the use of a deep learning Swin (shifted windows) Transformer model, by integrating high-resolution PlanetScope and Sentinel-2 satellite imagery, can effectively enhance wildfire damage detection performance. A 5-fold cross-validation was conducted on five major wildfire areas in South Korea, with independent detection performance evaluated for each region to assess the model’s reliability and generalization. The comparison between traditional geometric augmentation methods and the AugMix method showed that AugMix resulted in a more consistent and improved performance, achieving an average F1-score of 76.48. Furthermore, the model incorporating Sentinel-2 data achieved an average F1-score of 81.06 across regions, confirming an improvement in detection accuracy and performance in identifying burned areas. This demonstrates that combining the complementary spatial and spectral information from both datasets allows for higher accuracy and precision in detecting wildfire damage. The high correlation coefficients between PlanetScope and Sentinel-2 data support the effective potential of multi-satellite fusion approaches. This study suggests that the use of multi-satellite imagery, through independent regional evaluation, can enhance the precision and reliability of wildfire damage detection. Copyright © 2024 Korean Society of Remote Sensing.

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