Very High- to High- Resolution Imagery Transferability for Building Damage Detection Using Generative AI

Ali Shibli, Andrea Nascetti, Yifang Ban

Abstract: Wildfires are a growing global concern, causing significant damage to urban infrastructure each year. This study presents a novel approach for building damage assessment using generative artificial intelligence, focusing on the transferability of high-resolution satellite imagery models to lower-resolution datasets. Our diffusion-based model is trained on the xView2 Wildfire Building Damage Benchmark, a dataset specifically designed for wildfire-induced building damage detection. The model is further evaluated on real-world wildfire incidents in Lahaina, Hawaii, and Athens, Greece, demonstrating its effectiveness in damage localization across varying spatial resolutions. With competitive performance on benchmark datasets and practical utility in real-world scenarios, this work highlights the potential of generative AI for geospatial disaster assessment and urban resilience.

External IDs:dblp:conf/jurse/ShibliNB25