Building Inclination Measurement Based on Point Cloud Data

Measuring building inclination and its related horizontal displacement is crucial for evaluating structural performance. Conventional methods, often reliant on surveying instruments such as theodolites, face numerous limitations that can compromise measurement accuracy. This study proposes a point cloud–based building inclination measurement method that combines georeferenced point cloud acquisition using an unmanned aerial vehicle–mounted three-dimensional (3D) light detection and ranging scanner and a global navigation satellite system with a geometry-based computational procedure using 3D coordinates. The proposed workflow computes directional inclination values and evaluates inclination ranks. The method was applied to three field cases (single-building and multiple-building applications) and compared with conventional measurements. In addition, robotic total station (RTS)-based measurements were used as an external reference for accuracy verification in feasible directions. The results show that the proposed method provides consistent inclination rankings and close agreement with RTS-based measurements, while reducing operator-dependent variability observed in conventional measurements. The proposed method is suitable for practical field applications and provides a scalable workflow for inclination assessment of multiple structures.