An adaptable hybrid method for lossless airborne lidar data compression

Light Detection and Ranging (LIDAR) point clouds provide high precision spatial data but impose significant storage and transmission challenges, often exceeding one gigabyte per square kilometer. This paper introduces a novel hierarchical framework for lossless LiDAR data compression, designed to address these issues through a three-stage approach: class-aware segmentation, adaptive algorithm selection, and hierarchical compression. The framework begins by partitioning point clouds into semantic classes (e.g., ground, vegetation, buildings) using an SVM-based classifier with a radial basis function kernel, enabling targeted compression that exploits intra-class redundancies. The adaptive algorithm selection stage employs a density-based matcher to choose optimal compression algorithms for each class, ensuring efficiency across varying point densities and terrain types. Finally, hierarchical compression merges class-specific compressed files and applies a secondary compression using WinRAR for enhanced efficiency. Evaluated on ten openly available benchmark LiDAR datasets, the proposed method consistently outperforms state-of-the-art lossless compression techniques, such as LASzip, achieving file size reductions to 12.76% of the original for high-density point clouds and 22.51% for low-density ones. While compression and decompression times are higher than some alternatives, the framework’s superior storage savings and perfect fidelity make it ideal for large-scale LiDAR data archiving and exchange. • Novel compression method: A new approach significantly reduces LiDAR data size. • Exceptional compression results: Achieves up to 87% reduction in data size. • Improved LiDAR data management: Efficiently handles massive LiDAR datasets.