Spiking-Siamese-UNet with Multi-Threshold Spiking Neurons for Post-Disaster Building Damage Assessment

Spiking Neural Networks (SNNs) have shown potential for building damage detection after natural disasters. However, most existing methods focus solely on basic segmentation tasks, limiting their ability to assess detailed post-disaster damage. The conversion from Artificial Neural Networks (ANNs) to SNNs presents challenges, such as energy inefficiency and information loss. To address these limitations, we propose a novel Spiking-Siamese-UNet model that combines SNNs, Siamese Networks, and UNet architectures to enhance building damage classification from satellite imagery. By introducing multi-threshold spiking neurons and connection-wise normalization, and utilizing the xBD dataset, the model captures structural changes and damage severity with high precision. Experimental results demonstrate that the Spiking-Siamese-UNet outperforms traditional approaches in both damage localization and classification, offering an efficient solution for scalable disaster damage assessment, Our code is available in https://github.com/slzhang01/Spiking-Siamese-UNet.