With the rapid development of sensing platforms, unmanned aerial vehicle (UAV)-based mapping has become increasingly popular because of its economic efficiency and flexibility, especially for providing 3D information to support urban growth monitoring and change detection to meet sustainable development goals (SDGs). This paper presents an improved optical flow field-based dense matching algorithm (OFFDM) for low-altitude UAV images based on the Ph.D. thesis of Yuan (Yuan, 2018). First, high-precision seed points were used to compute the optical flow field within stereo pairs, effectively minimizing redundant calculations during the fine-matching phase. Second, a fine-matching approach, integrating multiple constraints, was applied to refine the coarse matching results based on the optical flow field. Extensive dense matching experiments on UAV low-altitude aerial imagery assessed the performance of OFFDIM across four dimensions: 3D point cloud visualization, matching success rate, precision, and reliability. Extensive experiments on low-altitude UAV imagery, characterized by a resolution of 7cm per pixel over a 10,608×8,608 pixel dimension and a 60% forward overlap, evaluate the OFFDM's efficacy. The quantitative evaluation revealed that the proposed method achieved an accuracy of ±0.7 pixels in image coordinates and ±20 cm on the ground, with a matching success rate exceeding 97%. The processing time was approximately 272 seconds for handling one single stereo pair. When compared to the widely adopted PMVS algorithm, known for its effectiveness in dense matching for UAV images, the proposed method demonstrated higher completeness and improved matching efficiency by more than five times. These results demonstrated that the proposed approach is more suitable for dense matching on UAV imagery-based high-precision 3D spatial data extraction, supporting global mapping tasks more effectively.