High-resolution satellite imagery (HRSI) and airborne light detection and ranging (LiDAR) data are widely used for deriving 3D spatial information. However, the 3D spatial information derived from them in the same area can be inconsistent. Considering HRSI and LiDAR datasets taken from metropolitan areas as a case study, this paper presents a novel approach to the geometric integration of HRSI and LiDAR data to reduce their inconsistencies and improve their geopositioning accuracy. First, the influences of HRSI’s individual rational polynomial coefficients (RPCs) on geopositioning accuracies are analyzed and the RPCs that dominate those accuracies are identified. The RPCs are then used as inputs in the geometric integration model together with the tie points identified in stereo images and LiDAR ground points. A local vertical constraint and a local horizontal constraint are also incorporated in the model to ensure vertical and horizontal consistency between the two datasets. The model improves the dominating RPCs and the ground coordinates of the LiDAR points, decreasing the inconsistencies between the two datasets and improving their geopositioning accuracy. Experiments were conducted using ZY-3 and Pleiades-1 imagery and the corresponding airborne LiDAR data in Hong Kong. The results verify that the geometric integration model effectively improves the geopositioning accuracies of both types of imagery and the LiDAR points. Furthermore, the model enables the full comparative and synergistic use of remote sensing imagery and laser scanning data collected from different platforms and sensors.