Multi-feature driven rapid inspection of earthquake-induced damage on building facades using UAV-derived point cloud

Rapid post-earthquake structural assessments are essential, as they significantly contribute to community recovery and enhancing seismic resilience. This research introduces a novel vision-based approach for rapid damage inspection of building facades affected by earthquakes. It employs structural point cloud models, derived from unmanned aerial vehicles (UAVs) imagery, to detect surface damages like spalling and cracks on facades. The developed algorithm effectively identifies these damages through a strategic consideration of three critical features: depth, grayscale, and local Principal Component Analysis (PCA). The study explores the impact of integrating these features on the accuracy and efficiency of damage segmentation. By adopting two real-world seismic-damaged buildings, the algorithm achieved a relatively high segmentation precision for wall cracking and spalling. Furthermore, the research provides a detailed assessment of wall damage, including quantifications of damage distribution, damaged wall areas, crack skeletons, and overall dimensions.