Digital Earth frameworks provide a tool to receive, send and interact with large location-based datasets, organized usually according to Discrete Global Grid Systems (DGGS). In DGGS, an indexing method is used to assign a unique index to each cell of a global grid, and the datasets corresponding to these cells are retrieved or allocated using this unique index. There exist many methods to index cells of DGGS. Toward facility, interoperability and also defining a "standard" for DGGS, a conversion is needed to translate a dataset from one DGGS to another. In this paper, we first propose a categorization of indexing methods of DGGS and then define a general conversion method from one indexing to another. Several examples are presented to describe the method.

Discrete global grid systems (DGGS) are spatial references that use a hierarchical tessellation of cells to partition and address the entire globe. DGGS are designed to portray real‐world phenomena by synthesizing digital values on a common discrete geospatial data structure. DGGS provide an organizational structure that permits fast integration between multiple sources of large and variable geospatial data sources sufficient for fast web‐based visualization and analysis. They are commonly used to create virtual globes. The adoption of an optimized DGGS is considered a favorable choice for the establishment of distributed Digital Earth information systems. A DGGS is designed to be an information grid, not a navigation grid. DGGS provide a reference frame for repeating the location of measured earth observations, feature interpretations, and extrapolated predictions. Information integration, decomposition, and aggregation can be optimized in the hierarchical structure, which can be exploited to support data processing, storage, discovery, transmission, visualization, computation, analysis, and modeling.

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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.

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