The filtering process of spaceborne LiDAR data is susceptible to interference from complex backgrounds, gross errors, noise points, and other issues, resulting in a significant decrease in filtering effectiveness. Therefore, a multi-scale filtering method for spaceborne LiDAR data based on improved DBSCAN is studied. The improved DBSCAN algorithm is used to cluster spaceborne LiDAR data, label noise points, and extract point cloud data features using a hemispherical neighborhood algorithm. Based on the extracted point cloud data features, a regular grid is constructed, and the coarse points and noise points in the point cloud data are removed through the multipath effect of the grid, completing multi-scale filtering of spaceborne LiDAR data. The experimental results show that the proposed method has low multi-scale filtering error and good filtering effect for spaceborne LiDAR data, and has high practical application value.