Abstract: Airborne LiDAR equipment is susceptible to terrain undulations, flight heights, specular reflections and other factors, which leads to holes in LiDAR data. Moreover, if the holes are not detected or processed, it will seriously affect the production and application of LiDAR data. In view of this, this paper uses the position and attribute information of the point cloud, based on the equal-scale shrinkage and point cloud rasterization algorithm to detect the vulnerability areas in the airborne LiDAR data, and then supports the quality detection of the LiDAR data and the supplementary scanning of the vulnerability areas. Experimental results show that the vulnerability area detected by this method is complete and accurate, and the proportional reduction algorithm reduces the influence of terrain fluctuations on the vulnerability detection accuracy compared to the equivalent reduction algorithm, which is more scientific and reasonable.

Key words: laser point cloud, vulnerability detection, shrink in the same proportion, survey area coverage