Abstract: Using remote sensing to detect changes in urban buildings can obtain the change information of building coverage quickly and accurately. However, it is difficult to detect 3D changes quickly and accurately based on image data alone. Moreover, conventional point cloud-based methods have low automation and poor precision. To address these problems, this study used the airborne LiDAR point clouds and employed the RandLA-Net’s point cloud semantic segmentation method to improve the accuracy and automation of change detection. Meanwhile, the failure in differentiating two-period data due to point cloud disorder was overcome through point cloud projection. The standard RandLA-Net method, with the location and color information of points as features, is mainly used for semantic segmentation of street-level point clouds. In this study, urban large-scale airborne point clouds combined with the inherent reflection intensity and the spectral information of point clouds given by images were used to explore the influence of different feature information on the precision of the results. Furthermore, it was found that in addition to the point cloud intensity and spectral features, the coordinate information of points is equally important and can be converted into relative coordinates to significantly improve the result precision. The experimental findings show that the results obtained using RandLA-Net are significantly better than those using conventional methods for building extraction and change detection. This study also verified the feasibility of using deep learning methods to process LiDAR data for building extraction and change detection, which can realize reliable 3D building change detection.