A general-purpose framework for parallel processing of large-scale LiDAR data

作     者：Zhenlong Li Michael E.Hodgson Wenwen Li 

作者机构：Department of GeographyUniversity of South CarolinaColumbiaSCUSA School of Geographical Sciences and Urban PlanningArizona State UniversityTempeAZUSA 

出 版 物：《International Journal of Digital Earth》 (国际数字地球学报（英文）)

年 卷 期：2018年第11卷第1期

页      面：26-47页

核心收录：

学科分类：08[工学] 0708[理学-地球物理学] 0835[工学-软件工程] 0704[理学-天文学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This study was funded by University of South Carolina through the ASPIRE(Advanced Support for Innovative Research Excellence)program[13540-16-41796] Additional funding was provided by the South Carolina Department of Transportation under contract to the University of South Carolina[SPR#707 or USC 13540FB11] USGS[G15AC00085] NSF-BCS 

主　　题：Big data online geoprocessing Hadoop MapReduce spatial decomposition LAStools parallel 

摘      要：Light detection and ranging(LiDAR)data are essential for scientific discoveries such as Earth and ecological sciences,environmental applications,and responding to natural *** collecting LiDAR data over large areas is quite possible the subsequent processing steps typically involve large computational *** storing,managing,and processing LiDAR data are the prerequisite steps for enabling these LiDAR-based ***,handling LiDAR data poses grand geoprocessing challenges due to data and computational *** tackle such challenges,we developed a general-purpose scalable framework coupled with a sophisticated data decomposition and parallelization strategy to efficiently handle‘big’LiDAR data *** contributions of this research were(1)a tile-based spatial index to manage big LiDAR data in the scalable and fault-tolerable Hadoop distributed file system,(2)two spatial decomposition techniques to enable efficient parallelization of different types of LiDAR processing tasks,and(3)by coupling existing LiDAR processing tools with Hadoop,a variety of LiDAR data processing tasks can be conducted in parallel in a highly scalable distributed computing environment using an online geoprocessing application.A proof-of-concept prototype is presented here to demonstrate the feasibility,performance,and scalability of the proposed framework.