AB2CD: AI for Building Climate Damage Classification and Detection

Abstract

We explore the implementation of deep learning techniques for precise building damage assessment in the context of nat- ural 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 in- fluence of low-quality and noisy labels inherent in natural hazard data. Furthermore, our investigation establishes the minimum satellite imagery resolution essential for effective building damage detection. To achieve robust and accurate evaluations of building damage detection and classification, we integrate deep learning models, ensemble techniques, and meta-learners. Our research findings showcase the potential 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.