AFTERMAP: A FEMA-Aligned Dataset for Damaged Structure Segmentation in UAV Imagery

Abstract

Rapid and accurate building damage assessment is essential for effective post-disaster response, yet the development of reliable artificial intelligence (AI) models remains constrained by the limited availability of high-quality, operationally relevant benchmark datasets. To address this gap, this paper presents AFTERMAP (Aerial FEMA-Aligned Targeted Extraction and Reconstruction Mapping for Post-Disaster Building Damage), a benchmark UAV imagery dataset for instance segmentation of post-disaster building damage. The dataset contains 1926 high-resolution UAV images with pixel-level annotations for five building damage categories, following FEMA Preliminary Damage Assessment (PDA) guidelines where applicable: Destroyed, Major Damage, Minor Damage, Tarp, and No Damage. Using AFTERMAP, we benchmarked recent YOLO-based instance segmentation models and evaluated their performance using mAP50 and mAP50-95. Among the evaluated models, YOLO26-L achieved the best performance, obtaining a test mAP50 of 0.600 and outperforming both Mask R-CNN (0.524) and Mask2Former (0.368) in cross-architecture comparisons. Model generalization was further demonstrated through an independent case study using UAV imagery collected after the 2025 Somerset–London tornado in Kentucky, where the trained model successfully identified building-level damage patterns under real-world field conditions. The results demonstrate that AFTERMAP provides a challenging and realistic benchmark for UAV-based post-disaster building damage assessment. By combining FEMA-aligned annotations with high-resolution UAV imagery, the dataset establishes a standardized resource for developing and evaluating instance segmentation models that can support rapid post-disaster damage assessment and emergency response.