MCANet: A Multi-Scale Class-Specific Attention Network for Multi-Label Post-Hurricane Damage Assessment Using UAV Imagery

תקציר מקורי באנגליתarXiv:2509.04757v2 Announce Type: replace-cross Abstract: Hurricanes cause widespread damage to buildings, roads, and other infrastructure, making timely post-disaster damage assessment critical for emergency response and recovery planning. Unmanned aerial vehicle (UAV) imagery provides high-resolution observations of affected areas, but post-hurricane scenes are difficult to classify because multiple damage categories often co-occur within the same image, appear at different spatial scales, and include visually similar severity levels as well as rare but operationally important classes. To address these challenges, this study presents MCANet, a multi-label classification framework for post-hurricane UAV damage assessment. MCANet integrates a Res2Net-based backbone for multi-scale represen