Taking the point cloud data from unmanned aerial vehicle (UAV) images of expressways as the research object, this study proposes a road green belt segmentation algorithm based on double decision factors. For this purpose, the point cloud data is serially down-sampled to retain as many point cloud feature points as possible in addition to reducing the number of point clouds; then, orthorectification of the down-sampled point cloud data is performed; finally, a point cloud segmentation algorithm featuring double decision with the normal vector angle and random sample consensus (RANSAC) plane segmentation is proposed, and accurate segmentation of the green belts in expressways is thereby achieved. The information on the environment of expressways is ultimately segmented with the green belt boundary extraction algorithm. Taking the point cloud from the UAV images of the Fengxiang section of G85 Expressway as the experimental data, this study verifies the proposed algorithm, the segmentation algorithm based on the normal vector angle, and the one based on RANSAC plane fitting. The experimental results show that the road green belt segmentation algorithm based on double decision factors can better resist the interference from environmental noise and outliers, effectively filter the high curvature points on the road surface, and ultimately obtain better extraction results.