ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2017, Vol. 54 ›› Issue (2): 267-283.doi: 10.7544/issn1000-1239.2017.20160837

所属专题: 2017科学大数据管理专题

• 软件技术 • 上一篇    下一篇

遥感大数据的基础设施:集成、管理与按需服务

李国庆1,黄震春2   

  1. 1(中国科学院遥感与数字地球研究所 北京 100094) 2(清华大学计算机科学与技术系 北京 100084) (ligq@radi.ac.cn)
  • 出版日期: 2017-02-01
  • 基金资助: 
    国家重点研发计划项目(2016YFB0501504); 海南省重大科技计划项目(ZDKJ2016021)

Data Infrastructure for Remote Sensing Big Data: Integration, Management and On-Demand Service

Li Guoqing1, Huang Zhenchun2   

  1. 1(Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094);2(Department of Computer Science and Technology, Tsinghua University, Beijing 100084)
  • Online: 2017-02-01

摘要: 随着遥感技术的不断进步,遥感数据的数据量越来越大,种类越来越多,分布越来越分散,遥感应用的复杂程度和个性化程度也不断提高,遥感正在走向大数据时代.而目前遥感数据基础设施在容量、可扩展性、易用性和性能等方面都难以满足遥感应用的需求,成为了遥感科学与工程从获取到最终产品这个流程中的瓶颈.为此,首先从遥感数据的本质出发,讨论了遥感数据基础设施应当具备的分布、异构、时空连续和按需数据处理等特性,并依据遥感数据基础设施的基本服务单元、分布性、时空连续性和按需处理支持能力将遥感数据基础设施分成6类.其次,针对这6类遥感数据基础设施展现出的特性,设计了实现这些基础设施可以采用的体系结构,并指出了其中实现的技术难点和解决思路.最后,就遥感数据基础设施设计和实现过程中涉及到的数据收集与整合、数据组织与管理、数据服务接口、按需数据处理等方面的技术方案进行了深入的讨论.在这些技术的支持下,遥感数据基础设施能够做到分布化、智能化和平台化,支持遥感科学的合作研究和工程上的协同应用.

关键词: 数据基础设施, 遥感大数据, 按需处理, 数据集成, 数据管理

Abstract: The increasing growth of remote sensing data and geoscience research pushes earth sciences strongly and poses great challenges to data infrastructures for remote sensing big data, including the collection, storage, management, analysis and delivery. The de-fact remote sensing data infrastructures become bottleneck of the workflows for remote sensing data analysis because of their capability, scalability and performance. In this paper, data infrastructures for remote sensing big data are catalogued into 6 classes based on the features such as basic service unit, distributivity, heterogeneous, space-time continuation and on-demand processing. Then, architectures are designed for all the 6 classes of data infrastructures, and some implementation technologies such as data collection and integration, data storage and management, data service interface, and on-demand data processing, are discussed. With the architecture designs and implementation technologies, data infrastructures for remote sensing big data will provide PaaS (platform-as-a-service) and SaaS(software-as-a-service) services for developing much more remote sensing data analysis applications. With continuously growing data, tools and libraries in the infrastructures, users can easily develop analysis models to process remote sensing big data, create new applications based on these models, and exchange their knowledge each other by sharing models.

Key words: data infrastructure, remote sensing big data, on-demand processing, data integration, data management

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