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.

Discrete global grid systems (DGGSs) are considered to be promising structures for global geospatial information representation. Square and triangular DGGSs have had the advantage over hexagonal ones in geospatial data processing over the past few decades. Despite a significant body of research supporting hexagonal grids as the superior alternative, the application thereof has been hindered partly owing to the lack of a hierarchy. This study presents an original perspective to combine two types of aperture 4 hexagonal discrete grid systems into a hierarchy. Each cell of the hierarchy is assigned a unique code using a linear quadtree that constructs the hexagonal quaternary balanced structure (HQBS). The mathematical system described by HQBS addressing and the vector operations, including addition, subtraction, multiplication, and division, are defined. Essential spatial operations for HQBS cell retrieval, transformation between HQBS codes and other coordinate systems, and arrangement of HQBS cells on spherical surfaces were studied and implemented. The accuracy and efficiency of algorithms were validated through experiments. The results indicate that the average efficiency of cell retrieval using the HQBS is higher than that using other schemes, thus proving it to be more efficient.

Datacubes are increasingly being implemented to manage big data workflows efficiently, particularly those for processing geospatial data. However, there is confusion in both the definition of the term “datacube” and the choices for how it is implemented. This and the conventional approach to managing spatial data (i.e., in map-projected data sets) have led to a restricted set of datacube implementations that are each tightly coupled to the spatial constraints of the data and how they are stored on disc – resulting in barriers to interoperability, particularly on global scales. This article discusses options and how it is possible to implement a datacube based on discrete global grid systems, while using the same topologies as conventional datacubes. These provide a flexible spatial data infrastructure that leverages the same topological advantages as conventional geospatial datacubes, while reducing barriers to data interoperability of both raster and vector data and providing additional functionality. Also, they potentially provide a very efficient approach to connecting to big data sources in order to extract datasets on demand prior to proceeding to multi-level intelligent big data processing, mining, machine learning, and visualizations.

The polyhedral discrete global grid system (DGGS) is a multi-resolution discrete earth reference model supporting the fusion and processing of multi-source geospatial information. The orientation of the polyhedron relative to the earth is one of its key design choices, used when constructing the grid system, as the efficiency of indexing will decrease if local areas of interest extend over multiple faces of the spherical polyhedron. To date, most research has focused on global-scale applications while almost no rigorous mathematical models have been established for determining orientation parameters. In this paper, we propose a method for determining the optimal polyhedral orientation of DGGSs for areas of interest on a regional scale. The proposed method avoids splitting local or regional target areas across multiple polyhedral faces. At the same time, it effectively handles geospatial data at a global scale because of the inherent characteristics of DGGSs. Results show that the orientation determined by this method successfully guarantees that target areas are located at the center of a single polyhedral face. The orientation process determined by this novel method reduces distortions and is more adaptable to different geographical areas, scales, and base polyhedrons than those employed by existing procedures.

Hexagonal discrete global grid systems are multi-resolution frameworks for earth data and have attracted much attention in research towards the next generation of geographic information system. Each resolution of the framework divides the earth into a hexagonal grid of cells and cell codes are used instead of Cartesian coordinates to organize and query data. Aperture (the ratio of cells between resolutions) 4 systems have structural advantages compared with aperture 3 and 7, but haven’t been fully developed. In this manuscript, we examine the aperture 4 hexagon hierarchy on uniform tiles (HHUT) and modify the coding method. We propose two HHUT-based cell navigation methods, one for regular and the other for irregular areas. We demonstrate the utility and high efficiency of the proposed methods in two applications. Results show that the proposed method for an irregular area is on average 400 times faster than DGGRID, the conventional library. The proposed method for a regular area is on average seven times faster than the hexagon lattice quad tree method and approaches that of H3. The modified coding and novel cell navigation methods proposed here indicate high efficiency, superior data organization and broad application prospects.