Abstract
Conference Title: 2018 14th International Computer Engineering Conference (ICENCO) Conference Start Date: 2018, Dec. 29 Conference End Date: 2018, Dec. 30 Conference Location: Cairo, Egypt Airborne LiDAR scanning systems are one of the most advanced remote sensing systems. They are capable to rapidly cover large geographical areas to gather data from with very high precision and great density. As a result, obtained datasets can contain tens of millions of points which consume more than a gigabyte per square kilometers (GB/km2). In practice, significant problems had been issued such as expensive storage, difficult distribution to the users, and time-consuming exchange over the internet and data processing and display time. For these reasons, LiDAR data compression has become recently a critical issue. In this paper, we compared three LiDAR domain-specific compression algorithms (LASzip, LASComp, and LiDAR Compressor) against three general-purpose compression algorithms (7-Zip, WinZip, and WinRAR). We have used real Airborne LiDAR point clouds data for Washington, DC (District of Columbia) for doing the experiments to reflect real LiDAR data compression issues. In this work, the algorithms had been evaluated in terms of Compression Ratios, Compression Times, and Bits per Point. Also, we have evaluated effects of point cloud density and number of contained points on the compression efficiency. Experiment results indicated that LASzip algorithm outperforms other algorithms with average compression ratio achieved 16.63% and average compression time achieved 16.65 sec. on the other hand, the general-purpose compression algorithm (WinRAR) surpass the LiDAR domain-specific compression algorithm (LiDAR Compressor) with compression ratio achieved 20.24%.