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引用本文:

DOI:

10.11834/jrs.20210566

收稿日期:

2020-12-15

修改日期:

2021-06-28

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GeoCube: 面向大规模分析的多源对地观测时空立方体
高凡1, 乐鹏1, 姜良存1, 曹志鹏1, 梁哲恒2, 上官博屹1, 胡磊1, 赵帅锋1
1.武汉大学 遥感信息工程学院;2.广东南方数码科技股份有限公司
摘要:

随着对地立体观测体系的建立,遥感大数据不断累积。传统基于文件、景/幅式的影像组织方式,时空基准不够统一,集中式存储不利于大规模并行分析。对地观测大数据分析仍缺乏一套统一的数据模型与基础设施理论。近年来,数据立方体的研究为对地观测领域大数据分析基础设施提供了前景。基于统一的分析就绪型多维数据模型和集成对地观测数据分析功能,可构建一个基于数据立方的对地观测大数据分析基础设施。因此,本文提出了一个面向大规模分析的多源对地观测时空立方体,相较于现有的数据立方体方法,本文强调多源数据的统一组织、基于云计算的立方体处理模式以及基于人工智能优化的立方体计算。研究有助于构建时空大数据分析的新框架,同时建立与商业智能领域的数据立方体关联,为时空大数据建立统一的时空组织模型,支持大范围、长时序的快速大规模对地观测数据分析。本文在性能上与开源数据立方做了对比,结果证明提出的多源对地观测时空立方体在处理性能上具有明显优势。

GeoCube: A multi-source EO cube towards large-scale analysis
Abstract:

The volume of Earth Observation (EO) data has a tremendous increase after the establishment of EO system. Managing such big EO data and turning them into valuable information is a big challenge in EO domain. Traditional paradigm organizes data with scenes granularity and stores them in a data center, which can lead inefficient use of high performance infrastructures. Recent advocate of data cube in EO domain has shown great promise on accessing, analyzing and visualizing big EO data. A data cube based infrastructure can be developed by integrating uniform analysis ready data and related EO analysis. This paper proposed a multi-source EO Cube towards large-scale analysis, named GeoCube. Compared with existing cube approaches, the infrastructure emphasizes the accommodation of multi-source geospatial data including raster and vector data in the cube, cube tile processing with cloud computing, and Artificial Intelligence (AI) machine learning enabled cube computation. This paper not only helps establish a new framework for big geospatial data management and analysis, but also creates a connection with data cube in business intelligence (BI) domain. This paper compares the performance of the OpenDataCube and the proposed GeoCube infrastructure, the results demonstrate the advantage of GeoCube.

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