Abstract. Post-disaster rapid response relies on the timely acquisition of information. Specifically, assessing structural integrity of building requires fast and reliable analysis, which can be supported by quantitative damage assessment based on the complete 3D geometry knowledge of structures. Such information can be derived either from quickly acquired imagery (e.g., video frames or panoramas) or directly from 3D data. Today, data collection is relatively straightforward thanks to UAV surveys and mobile mapping systems; however, extracting actionable information remains time-consuming when performed manually. This highlights the need for automated methods that can localize damage, identify critical issues, and support interpretation and decision-making. In recent years, artificial intelligence (AI) has attracted substantial attention in this domain, driven by the emergence of models that deliver fast and effective performance across a range of perception tasks. Yet the success of these approaches remains strongly conditioned by data availability and quality. In many application settings—especially those involving rare or highly specific post-disaster scenarios—representative training samples are scarce, underscoring the need for dedicated datasets construction, together with methods capable of learning from limited data. In this research, a dataset dedicated to the detection of cracks was collected. The aim is to have quick and straightforward identification of decay related to the structural stability of the building. Trained YOLOv11 object detection and segmentation model were tested on two case studies collected in Lebanon. These two case studies feature structural damage under severe external force, representative for the post-disaster scenarios analysis. The research evaluated the proposed hybrid approach (involving deep learning and machine learning methods and integrated with photogrammetric workflow), retrieving architectural severe damage location from 2D and localizing them effectively in 3D to assess global analysis, by comparing data with the ground truth in both 2D and 3D data.
Zhang et al. (Thu,) studied this question.