CalFireSegNet: a lightweight hybrid attention-transformer network for post-wildfire building footprint change...

Natural disasters, particularly wildfires, cause severe human, environmental, and economic losses worldwide. Rapid and accurate identification of building footprints and potential potential structural changes is essential for effective emergency response, search-and-rescue operations, and post-disaster recovery planning. To address the challenges of identifying building loss from remote sensing imagery, this study proposes CalFireSegNet, a lightweight hybrid attention-transformer network for post-wildfire building footprint extraction and loss proxy detection from satellite imagery. The proposed architecture integrates depthwise convolutions, convolutional block attention modules (CBAM), atrous spatial pyramid pooling (ASPP), and Transformer blocks to effectively capture both local structural details and long-range contextual dependencies while maintaining low computational complexity. The model was trained and evaluated using benchmark building segmentation datasets (Inria and WHU) and subsequently applied to pre- and post-event satellite imagery from the recent California wildfires. Experimental results demonstrate that CalFireSegNet achieves superior performance compared with several state-of-the-art semantic segmentation models, including U-Net, PSPNet, DeepLabv3+, ENet, HRNet, and SegNet, obtaining 98.45% accuracy, 94.35% mIoU, and 95.03% Dice Similarity Score while requiring only 3.72 million parameters. Furthermore, a lightweight mask-difference framework was developed to generate a spatial proxy of potential building footprint loss using pre- and post-event satellite pairs. Since publicly available building-level damage annotations for recent California wildfire events remain limited, the real-world wildfire experiments are presented as a validation of cross-domain applicability rather than a fully supervised structural loss proxy estimation benchmark.