Abstract
Generative Adversarial Networks (GANs) offer a powerful framework for synthesizing post-disaster imagery, but their application to building damage assessment is limited by difficulties in generating realistic images of severely deformed structures, slow inference speeds that hinder real-time use in emergency scenarios, and a reliance on paired pre- and post-disaster data, which are rarely available in datasets collected by unmanned aerial vehicles (UAVs). To address these challenges, we propose Struct-CycleGAN, a cross-domain GAN model designed to generate high-fidelity and structurally consistent post-disaster building images from unpaired data. The model improves upon prior limitations in texture transformation and structural deformation synthesis while significantly enhancing inference speed for practical deployment. Compared to a commonly used state-of-the-art method, Struct-CycleGAN achieves nearly four times faster inference and obtains a perceptual quality score of 23.7 based on Fréchet Inception Distance (FID) evaluation. Further validation using a ResNet UNet segmentation framework shows that undamaged building areas in pre-disaster images decrease from 45% to 22% in the generated post-disaster outputs, demonstrating the model's effectiveness in simulating structural degradation. This work provides a scalable solution for synthesizing post-disaster building scenarios, supporting rapid damage assessment, emergency preparedness, and intelligent reconstruction efforts.
