SpADANet: A Spatially Aware Domain Adaptation Network for Hurricane Damage Assessment

Abstract

Hurricanes cause significant damage to communities, necessitating rapid and accurate damage assessment to support timely disaster response. However, image-based deep learning models for hurricane-induced damage assessment face substantial challenges due to domain shifts across different hurricane events, and the restricted availability of labeled data for each disaster further complicates this task. In this study, we propose a novel domain-adaptive deep learning framework that mitigates the domain gap while requiring minimal labeled samples from the target domain. Our approach integrates a self-supervised learning (SSL) pretext task to enhance feature robustness and leverages a novel bilateral local Moran’s I (BLMI) module to improve spatial feature aggregation for damage localization. We evaluate our method using aerial datasets from Hurricanes Harvey, Matthew, and Michael. The experimental results demonstrate that our model achieves more than 5% improvement in damage classification accuracy over baseline methods. These findings highlight the potential of our approach for scalable and efficient hurricane damage assessment in real-world disaster scenarios.