Integration of UAV remote sensing and GeoAI for rapid post-earthquake disaster monitoring

Rapid situational awareness is essential for seismic resilience in tectonically active regions such as Taiwan. For critical maritime infrastructure, traditional post-earthquake reconnaissance is often constrained by limited accessibility and safety concerns, leading to delays in disaster response. This study presents an automated disaster monitoring framework that integrates UAV remote sensing and Geospatial Artificial Intelligence (GeoAI) to quantify seismic impacts on wharf facilities. High-resolution aerial imagery and multi-temporal geospatial data are combined to establish a processing pipeline for identifying disaster footprints, with particular attention to the spatial distribution of structural fissures and surface deformations. A YOLOv11-based deep learning model is employed for automated damage detection and segmentation. To enable quantitative assessment, morphological skeletonization and three-dimensional spatial analysis are applied to derive geometric characteristics of damage features. The extracted information is further used to compute the Pavement Condition Index (PCI) as an indicator of facility serviceability. Experimental results show that the proposed framework achieves mAP and Recall values exceeding 90%, with a spatial localization accuracy of ±2 cm. The results demonstrate the capability of the proposed approach to reduce the time required for post-earthquake damage assessment and to support disaster monitoring and infrastructure management in seismically active maritime environments.