Cloud-based storage and computing for remote sensing big data:a technical review
作者机构：Key Lab of Digital Earth SciencesAerospace Information Research InstituteChinese Academy of SciencesBeijingPeople’s Republic of China International Research Center of Big Data for Sustainable Development GoalsBeijingPeople’s Republic of China College of Resources and EnvironmentUniversity of Chinese Academy of SciencesBeijingPeople’s Republic of China Institute for Environmental Sciences/GRID-GenevaUniversity of GenevaGenevaSwitzerland Institute for Marine and Atmospheric research Utrecht (IMAU)Utrecht UniversityUtrechtThe Netherlands State Key Laboratory of Computer ScienceInstitute of SoftwareChinese Academy of SciencesBeijingPeople’s Republic of China Tianyi CloudPeople’s Republic of China Telecom Co.LtdGuangzhouPeople’s Republic of China Department of Computer Science and TechnologyTsinghua UniversityBeijingPeople's Republic of China
出 版 物：《International Journal of Digital Earth》 (国际数字地球学报（英文）)
年 卷 期：2022年第15卷第1期
页 面：1417-1445页
核心收录：
学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：supported by Strategic Priority Research Program of the Chinese Academy of Sciences,Project title:CASEarth:[Grant Number XDA19080103,XDA19080101] Innovation Drive Development Special Project of Guangxi:[Grant Number GuikeAA20302022] National Natural Science Foundation of China:[Grant Number 41974108]
主 题：Remote sensing big data cloud computing data cube analysis ready data parallel computing data model
摘 要：The rapid growth of remote sensing big data(RSBD)has attracted considerable attention from both academia and *** the progress of computer technologies,conventional computing implementations have become technically inefficient for processing *** computing is effective in activating and mining large-scale heterogeneous data and has been widely applied to RSBD over the past *** study performs a technical review of cloud-based RSBD storage and computing from an interdisciplinary viewpoint of remote sensing and computer ***,we elaborate on four critical technical challenges resulting from the scale expansion of RSBD applications,*** storage,metadata management,data homogeneity,and computing ***,we introduce state-of-the-art cloud-based data management technologies for RSBD *** unit for manipulating remote sensing data has evolved due to the scale expansion and use of novel technologies,which we name the RSBD data *** data models are suggested,***,ARD,data cubes,and composite ***,we summarize recent research on the application of various cloud-based parallel computing technologies to RSBD computing ***,we categorize the architectures of mainstream RSBD *** research provides a comprehensive review of the fundamental issues of RSBD for computing experts and remote sensing researchers.
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