AI RESEARCH
DA-SegFormer: Damage-Aware Semantic Segmentation for Fine-Grained Disaster Assessment
arXiv CS.LG
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ArXi:2605.09864v1 Announce Type: cross Rapid and accurate damage assessment following natural disasters is critical for effective emergency response. However, identifying fine-grained damage levels (e.g., distinguishing minor from major roof damage) in UAV imagery remains challenging due to the degradation of texture cues during resizing and extreme class imbalance. We propose DA-SegFormer, a damage-aware adaptation of the SegFormer architecture optimized for high-resolution disaster imagery. Our method