Creating and visualising DGGS native data cubes with DGGS.jl

Discrete Global Grid Systems (DGGS) have emerged as a transformative approach to minimizing spatial distortions in geospatial data processing. They are not only used for geocoding, but also offer a highly efficient data structure due to the lack of tile overlap, as used in Sentinel-2 imagery and elsewhere. The performance of lookup operations on DGGS native data cubes is intrinsically linked to the cell index, which plays a crucial role in data management and retrieval. Most DGGS implementations utilize a hierarchical one-dimensional index to name and sort cells, optimizing them for parent-child queries like up and downsampling. However, many real-world applications, such as visualization or convolutions, require efficient handling of distant neighbour queries based on spatial distances.

Here, we present the tools DGGS.jl and DGGSexplorer to create and visualise DGGS native data cubes. Hereby, a three-dimensional index based on Icosahedral Snyder equal-area projection is utilized, enabling compact and efficient data cube arrays stored in the cloud-optimized Zarr format. Furthermore, we developed a XYZ Tile Map Server generating maps on the fly, allowing to view DGGS data in QGIS, in the web browser, and elsewhere.  This is especially helpful in integrating multi sensor data at different spatial resolutions while minimising spatial distortions and computational resources in all subsequent processing steps.