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

作者：Sultan Al Shafian, Chao He, Kiara O'Neal, Honghui Xu, Adeel Khalid, Da Hu · 发表于：Buildings · 年份：2026 · DOI：10.3390/buildings16152943 · 研究领域：3D Surveying and Cultural Heritage、Infrastructure Maintenance and Monitoring、Remote-Sensing Image Classification

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-disaste...