Deep-learning-based wildfire detection and transferability verification

Seonyoung Park; Jungho Im

doi:10.1117/12.3031320

20 November 2024 Deep-learning-based wildfire detection and transferability verification

Seonyoung Park, Jungho Im

Proceedings Volume 13191, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXVI; 131910W (2024) https://doi.org/10.1117/12.3031320
Event: Remote Sensing, 2024, Edinburgh, United Kingdom

Conference Poster

Abstract

This study focuses on the critical task of estimating wildfire-burned areas, utilizing remote sensing via cubesats to obtain high-resolution imagery for analysis. Using PlanetScope imagery and surface reflectance from visible and NIR bands, along with vegetation indices, the research mapped burned areas in two locations. A U-Net model based on CNN architecture was employed for estimations, with the data divided into training, testing, and validation sets. Model performance was assessed using metrics like IoU and F1-score, demonstrating the usefulness of vegetation indices combined with visible imagery for detecting burned areas, despite challenges from false alarms in snow and rocky terrains. The study also examined the model's transferability across different regions, indicating its adaptability despite variations in accuracy due to local conditions. This underscores the model's potential for broad geographic application in wildfire analysis.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Seonyoung Park and Jungho Im "Deep-learning-based wildfire detection and transferability verification", Proc. SPIE 13191, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXVI, 131910W (20 November 2024); https://doi.org/10.1117/12.3031320

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