The study presents the creation and application of the California Wildfire GeoImaging Dataset (CWGID), a high-resolution bi-temporal labeled satellite imagery dataset for deep learning-driven forest wildfire detection.
The dataset building process involves:
The resulting CWGID contains over 106,000 pairs of labeled before and after wildfire RGB GeoTIFF image tiles, with 29,082 positive instances of wildfire damage.
Three deep learning architectures - VGG16, Early Fusion (EF) EfficientNet-B0, and Siamese EfficientNet-B0 - are evaluated on the CWGID. The EF EfficientNet-B0 model achieves the highest accuracy of over 92% in detecting forest wildfires, outperforming the other approaches. The bi-temporal nature of the dataset allows this model to effectively capture changes between pre- and post-wildfire conditions.
The CWGID and the methodology used to build it prove to be a valuable resource for training and testing deep learning architectures for forest wildfire detection.
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by Valeria Mart... at arxiv.org 09-26-2024
https://arxiv.org/pdf/2409.16380.pdfDeeper Inquiries