GPU-based lightweight parallel processing toolset for LiDAR data for terrain analysis. (July 2019)

• Record Type: Journal Article
• Authors: Li, Jing; Xu, You; Macrander, Hailey; Atkinson, Laura; Thomas, Tabris; Lopez, Mario A.
• Is Part Of: Environmental modelling & software. Volume 117(2019)
• Page Start: 55
• Page End: 68
• Publication Date: 2019-07
• Subjects: LiDAR -- Parallel processing -- Graphics processing units; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2019.03.014
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: LiDAR products are provided at fine spatial resolutions and the data volume can be huge even for a small study region. Therefore, we have developed a parallel computing toolset that is built on Graphics Processing Units (GPUs) computing techniques to speed up the computational processes on LiDAR products. The toolset provides a set of fundamental processing functions for LiDAR point cloud data, serving as a basic toolkit to derive terrain data products. With this toolset, scientists with limited access to high-end computing facilities can still perform efficient analysis of LiDAR products without dealing with the technical complexity of developing and deploying tools for these products. We have integrated data decomposition methods to handle files that exceed the memory capacity of GPU devices. Preliminary results show that GPU-based implementation yields high speedup ratios and can handle files with a maximum size of 8 GB. Highlights: A GPU-based processing library for LiDAR data for terrain analysis was developed. A set of parallel strategies are formulated to various environmental (terrain) analyses. The data decomposition strategies allow the library to process large files. The library provides both source codes and executables for easy expansion and deployment.