Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges

• Main Authors: Xiaochuang Yao, Guoqing Li, Junshi Xia, Jin Ben, Qianqian Cao, Long Zhao, Yue Ma, Lianchong Zhang, Dehai Zhu
• Format: Article
• Language: English
• Published: MDPI AG 2019-12-01
• Series: Remote Sensing
• Subjects: big earth observation data; cloud computing; discrete global grid systems
• Online Access: https://www.mdpi.com/2072-4292/12/1/62

In the era of big data, the explosive growth of Earth observation data and the rapid advancement in cloud computing technology make the global-oriented spatiotemporal data simulation possible. These dual developments also provide advantageous conditions for discrete global grid systems (DGGS). DGGS are designed to portray real-world phenomena by providing a spatiotemporal unified framework on a standard discrete geospatial data structure and theoretical support to address the challenges from big data storage, processing, and analysis to visualization and data sharing. In this paper, the trinity of big Earth observation data (BEOD), cloud computing, and DGGS is proposed, and based on this trinity theory, we explore the opportunities and challenges to handle BEOD from two aspects, namely, information technology and unified data framework. Our focus is on how cloud computing and DGGS can provide an excellent solution to enable big Earth observation data. Firstly, we describe the current status and data characteristics of Earth observation data, which indicate the arrival of the era of big data in the Earth observation domain. Subsequently, we review the cloud computing technology and DGGS framework, especially the works and contributions made in the field of BEOD, including spatial cloud computing, mainstream big data platform, DGGS standards, data models, and applications. From the aforementioned views of the general introduction, the research opportunities and challenges are enumerated and discussed, including EO data management, data fusion, and grid encoding, which are concerned with analysis models and processing performance of big Earth observation data with discrete global grid systems in the cloud environment.