고밀도 항공라이다 3차원 점군자료 경량화 연구 = Study on Thinning for High-Density 3D Point Cloud Data of Airborne LiDAR

다국어 초록 (Multilingual Abstract)

High-density airborne LiDAR 3D point cloud data is widely used in various fields such as 3D object modeling, high-precision digital elevation model production, and vegetation and ecological environment analysis. Recently, advancements in airborne LiDAR equipment have enabled the acquisition of higher-density 3D point cloud data; however, the increased physical size of the data has posed limitations in expanding its usability. This study aims to explore efficient utilization methods for high-density airborne LiDAR-based 3D point cloud data. To achieve this, we analyzed the main functions of four representative point cloud thinning algorithms commonly used for data reduction, along with the characteristics of the specific parameters defined within each algorithm. We then conducted two experiments to determine the most optimized thinning algorithm and verify the quality changes in the simplified data based on parameter settings. In the first experiment, we applied four representative thinning algorithms―By density, by order, 2D grid, and 3D grid―to a high-density 3D point cloud data sample. The comparison of thinning performance showed that the 3D grid algorithm was the most suitable for reducing data size while preserving the quality of the original dataset. In the second experiment, we tested two parameters defined within the 3D grid algorithm: keep point and operation range. Among the 11 keep point parameter options, the ‘Central’ option achieved a vertical positional accuracy within an average root mean squared error (RMSE) of 0.06 meters when compared to all ground control points obtained through field surveying. It also showed a robust distribution with minimal error bias across all checkpoints. Furthermore, setting the grid size option of the operation range parameter to less than 1.00 meters resulted in a relative positional accuracy within 0.03 meters compared to the original data, maintaining object boundaries and shapes effectively. The correlation analysis between roof slope, vertical positional accuracy, and point cloud reduction rate revealed that roof slope significantly affects both vertical and reduction rates, indicating it as a dependent variable. A comparative analysis between the original and simplified 3D point cloud data for the same building showed that, while high-rise buildings allowed easier interpretation of boundaries and shapes, a grid size of 0.75 meters or less was sufficient for clear representation of most buildings. Using the vector data derived from the simplified datasets, we evaluated the horizontal positional accuracy based on roof shape and grid size. The results indicated that buildings with simpler boundaries were more sensitive to data loss from thinning, resulting in greater positional accuracy degradation. Finally, in terms of building area consistency, it was found that building height had a greater impact on the quality of the simplified data than area itself. The agreement rate of building area based on height was found to be 98% for high-rise buildings, 95% for mid-rise buildings, and 85% for low-rise buildings.