Abstract: The rapid development of the low-altitude economy creates an urgent demand for high-precision and computable urban 3D spatial data.As the core digital foundation for route planning and airspace management of low-altitude aircraft,the rapid and precise construction of LOD1.3 building models is crucial.However,existing methods suffer from issues such as low accuracy and high computational redundancy in roof facet segmentation.This paper proposes an efficient airborne LiDAR point cloud modeling method that integrates dynamic normal vector optimization with an adaptive RANSAC iteration strategy.By dynamically adjusting the neighborhood radius through curvature feedback and combining it with a point cloud density-adaptive RANSAC iteration,the method enhances the robustness of plane segmentation.It further integrates PCA fitting to optimize the geometric accuracy of roof facets.Experimental results show that the root mean square error of plane fitting reaches 0.11 m,representing a 47.6%reduction compared to traditional methods.The modeling efficiency achieves 1 km²/d,marking a 300% improvement.The segmentation accuracy reaches 86%,while the error rate for complex roofs decreases to 12.3%.This method provides a high-precision and low-cost LOD1.3 modeling solution for 3D real-scene construction,effectively supporting the digital and intelligent development of low-altitude economy applications.

Key words: low-altitude economy, city-level 3D model of LOD1.3, airborne LiDAR data, dynamic normal vector optimization, adaptive ransac, principal component analysis