Abstract
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.
Highlights
With the rapid development of Earth observation (EO) technology and continuous launch of remote sensing satellites, the resolution of Earth observation data is getting higher and higher, and the data quantity and variety are increasing, which indicate that EO data is gradually stepping into the era of big data [1]
To better solve the problem of global data processing, in this paper, we integrate the unified spatiotemporal unified framework, discrete global grid systems (DGGS), with big Earth observation data (BEOD) and cloud computing into a closed-loop solution
This paper mainly focuses on big Earth observation data and proposes a trinity solution consisting of BEOD, cloud computing, and DGGS, which separately provide a data resource, computing power, and a unified framework
Summary
With the rapid development of Earth observation (EO) technology and continuous launch of remote sensing satellites, the resolution of Earth observation data is getting higher and higher, and the data quantity and variety are increasing, which indicate that EO data is gradually stepping into the era of big data [1]. To better solve the problem of global data processing, in this paper, we integrate the unified spatiotemporal unified framework, discrete global grid systems (DGGS), with BEOD and cloud computing into a closed-loop solution. We analyze and discuss these three aspects In such a case, the Earth observation data should consider the unified data structure and combine the characteristics of cloud computing model to design and implement more favorable processing algorithms to maximize the mining and utilization of data values. The rest of this paper is organized as follows: Section 2 summarizes the development and main characteristics of BEOD. 2 summarizes the development and main challenges trinity solution of BEOD, cloudSection computing, and DGGS
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