  We explore the implementation of deep learning techniques for precise
building damage assessment in the context of natural hazards, utilizing remote
sensing data. The xBD dataset, comprising diverse disaster events from across
the globe, serves as the primary focus, facilitating the evaluation of deep
learning models. We tackle the challenges of generalization to novel disasters
and regions while accounting for the influence of low-quality and noisy labels
inherent in natural hazard data. Furthermore, our investigation quantitatively
establishes that the minimum satellite imagery resolution essential for
effective building damage detection is 3 meters and below 1 meter for
classification using symmetric and asymmetric resolution perturbation analyses.
To achieve robust and accurate evaluations of building damage detection and
classification, we evaluated different deep learning models with residual,
squeeze and excitation, and dual path network backbones, as well as ensemble
techniques. Overall, the U-Net Siamese network ensemble with F-1 score of 0.812
performed the best against the xView2 challenge benchmark. Additionally, we
evaluate a Universal model trained on all hazards against a flood expert model
and investigate generalization gaps across events, and out of distribution from
field data in the Ahr Valley. Our research findings showcase the potential and
limitations of advanced AI solutions in enhancing the impact assessment of
climate change-induced extreme weather events, such as floods and hurricanes.
These insights have implications for disaster impact assessment in the face of
escalating climate challenges.
