UAV based falling-object risk assessment of damaged building façades using visible and thermal infrared images

Extreme wind, heavy rainfall, solar radiation, and thermal cycling can accelerate the degradation of interfacial bonding in existing building façade systems. Conventional manual hammer sounding, gondola close range inspection, and image surveys based on a single modality are often inefficient, hazardous, and difficult to quantify spatially, limiting their applicability to rapid screening of large façade areas and emergency assessment around extreme weather events. To address this issue, this study proposes a falling object risk assessment method for damaged building façades using UAV visible light and thermal infrared images. The method is guided by the damage evolution from hollowing to spalling and falling. A dual branch recognition strategy is adopted, in which visible light images are used to detect apparent spalling defects, while thermal infrared images are used to identify thermal anomalies related to hollowing. The detected defect masks are then mapped onto a photogrammetric model to generate spatial defect objects with true surface area, elevation, façade location, and spatial relationship attributes. Based on the mapped defect area, potential falling impact energy, composite risk regions, exposure conditions, and extreme weather triggers, a screening framework that couples area and energy is established. A case study of a tile clad building in shows that the proposed method can transform UAV inspection outputs from image level defect masks into a spatial falling-object risk inventory. The results indicate that the framework can quantify defect geometry, identify high priority risk objects, and support façade safety screening, maintenance prioritization, and emergency inspection in extreme weather related scenarios.