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Building an Earth Observations Data Cube: lessons learned from the Swiss Data Cube (SDC) on generating Analysis Ready Data (ARD)

机译:建立地球观测数据立方体:从瑞士数据立方体(SDC)获得的有关生成分析就绪数据(ARD)的经验教训

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Pressures on natural resources are increasing and a number of challenges need to be overcome to meet the needs of a growing population in a period of environmental variability.?Some of these environmental issues can be monitored using remotely sensed Earth Observations (EO) data that are increasingly available from a number of freely and openly accessible repositories. However, the full information potential of EO data has not been yet realized. They remain still underutilized mainly because of their complexity, increasing volume, and the lack of efficient processing capabilities. EO Data Cubes (DC) are a new paradigm aiming to realize the full potential of EO data by lowering the barriers caused by these Big data challenges and providing access to large spatio-temporal data in an analysis ready form. Systematic and regular provision of Analysis Ready Data (ARD) will significantly reduce the burden on EO data users. Nevertheless, ARD are not commonly produced by data providers and therefore getting uniform and consistent ARD remains a challenging task. This paper presents an approach to enable rapid data access and pre-processing to generate ARD using interoperable services chains. The approach has been tested and validated generating Landsat ARD while building the Swiss Data Cube.
机译:对自然资源的压力越来越大,在环境多变的时期,要满足不断增长的人口的需求,就需要克服许多挑战。其中一些环境问题可以使用遥感的地球观测(EO)数据进行监测。越来越多的免费和开放式存储库提供了该功能。但是,尚未实现EO数据的全部信息潜力。由于它们的复杂性,数量的增加以及缺乏有效的处理能力,它们仍然未被充分利用。 EO数据多维数据集(DC)是一种新的范例,旨在通过降低这些大数据挑战造成的障碍并以分析就绪形式提供对大型时空数据的访问,从而实现EO数据的全部潜力。系统且定期提供分析就绪数据(ARD)将大大减轻EO数据用户的负担。然而,数据提供者通常并不生产ARD,因此获得统一一致的ARD仍然是一项艰巨的任务。本文提出了一种使用可互操作的服务链实现快速数据访问和预处理以生成ARD的方法。该方法已经过测试和验证,可在构建Swiss Data Cube时生成Landsat ARD。

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