计算机研究与发展DOI ：１０．７５４４∕issn １０００-１２３９．２０１７．２０１６０８３７Journal of Computer Research and Development ５４（２）：２６７２８３，２０１７ 收稿日期：２０１６－１１－１５；修回日期：２０１６－１２－２７
基金项目：国家重点研发计划项目（２０１６Y FB ０５０１５０４）；海南省重大科技计划项目（ZDKJ ２０１６０２１）
T his w ork w as supported by the National Key Research and Development Program of China （２０１６Y FB ０５０１５０４），and the Grant of Hainan Provincial Department of Science and T echnology （ZDKJ ２０１６０２１）． 通信作者：黄震春（huangzc ＠tsinghua ．edu ．cn ）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２１
（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 ）
ＤａｔａＩｎｆｒａｓｔｒｕｃｔｕｒｅｆｏｒＲｅｍｏｔｅＳｅｎｓｉｎｇＢｉｇＤａｔａ：Ｉｎｔｅｇｒａｔｉｏｎ，ＭａｎａｇｅｍｅｎｔａｎｄＯｎ-ＤｅｍａｎｄＳｅｒｖｉｃｅ
Li Guoqing １and Huang Zhenchun ２１
（ＩｎｓｔｉｔｕｔｅｏｆＲｅｍｏｔｅＳｅｎｓｉｎｇａｎｄＤｉｇｉｔａｌＥａｒｔｈ，ＣｈｉｎｅｓｅＡｃａｄｅｍｙｏｆＳｃｉｅｎｃｅｓ，Ｂｅｉｊｉｎｇ１０００９４）２（ＤｅｐａｒｔｍｅｎｔｏｆＣｏｍｐｕｔｅｒＳｃｉｅｎｃｅａｎｄＴｅｃｈｎｏｌｏｇｙ，ＴｓｉｎｇｈｕａＵｎｉｖｅｒｓｉｔｙ，Ｂｅｉｊｉｎｇ１０００８４）
Ａｂｓｔｒａｃｔ T he increasing grow th 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 ．T he de -fact remote sensing data infrastructures become bottleneck of the w orkflow s 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 ６classes based on the features such as basic service unit ，distributivity ，heterogeneous ，space -time continuation and on -demand processing ．T hen ，architectures are designed for all the ６classes of data infrastructures ，and some implementation technologies such as data collection and integration ，data storage and management ，data service interface ，and on -demand data p rocessing ，are discussed ．With the architecture designs and implementation technologies ，data infrastructures for remote sensing big data will provide PaaS （p latform -as -a -service ）and SaaS （softw are -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 know ledge each other by sharing models ．Ｋｅｙｗｏｒｄｓ data infrastructure ；remote sensing big data ；on -demand processing ；data integration ；data management
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、时空连续性万方数据