Abstract: To address the challenges of basemap construction and obstacle detection in unmanned aerial vehicle path planning within complex urban low-altitude environments, this paper proposes a BeiDou grid code-assisted method for unmanned aerial vehicle low-altitude flight basemap construction. This method leverages the regularized spatial partitioning capability of the BeiDou grid code to dynamically subdivide and refine the grid structure in obstacle-occupied urban areas. By projecting geographic information vector data onto grid units, a lightweight and adaptive basemap is constructed, overcoming the limitations of traditional basemaps in terms of data volume, computational efficiency, and applicability. The proposed method significantly reduces the computational complexity of path planning. Experimental results demonstrate that, compared with the original geospatial vector data, the BeiDou grid code-based data volume is stably reduced by more than 95%. Furthermore, the A* algorithm was successfully employed in path planning tests to generate collision-free paths, achieving a threefold improvement in computational efficiency.