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The paper presents DamageCAT, a deep learning transformer framework for categorizing building damage post-disaster using satellite imagery. It introduces the BD-TypoSAT dataset with four damage categories and a hierarchical U-Net-based architecture that enhances damage assessment accuracy, achieving an overall IoU of 0.737 and F1-score of 0.846. This framework aims to provide more actionable insights for emergency response and resource allocation compared to traditional severity-based approaches.

Author

svishnu.personalacc

Language

EN