Concept and Framework of 3D Geo-Spatial Grid System

Yoo, Ji Song; Min, Kyung Ju; Ahn, Jong Wook

doi:10.1007/978-3-030-17246-6_11

Cited by 4 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It also makes data querying less efficient, as data at all altitudes associated with a cell must be searched to find those that correspond with the desired altitude(s). With the large amounts of 3D geospatial data being generated, such as 3D point clouds from LIDAR [33] and aircraft flight paths [34,35], there is emerging an increasing need for 3D global grids and grid systems [12].…”

Section: Related Workmentioning

confidence: 99%

“…While DGGSs are effective for integrating and managing geospatial data on the surface of the Earth, they have no inbuilt mechanisms for supporting 3D data. Because of this, there has been a recent push toward developing technologies for 3D global grids and grid systems [12]. While most 3D geospatial data are located in a relatively small region above and below the surface of the Earth, vital processes such as seismic wave propagation and the magnetosphere extend much beyond this narrow region.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

General Method for Extending Discrete Global Grid Systems to Three Dimensions

Ulmer

Hall

Samavati

2020

IJGI

View full text Add to dashboard Cite

Geospatial sensors are generating increasing amounts of three-dimensional (3D) data. While Discrete Global Grid Systems (DGGS) are a useful tool for integrating geospatial data, they provide no native support for 3D data. Several different 3D global grids have been proposed; however, these approaches are not consistent with state-of-the-art DGGSs. In this paper, we propose a general method that can extend any DGGS to the third dimension to operate as a 3D DGGS. This extension is done carefully to ensure any valid DGGS can be supported, including all refinement factors and non-congruent refinement. We define encoding, decoding, and indexing operations in a way that splits responsibility between the surface DGGS and the 3D component, which allows for easy transference of data between the 2D and 3D versions of a DGGS. As a part of this, we use radial mapping functions that serve a similar purpose as polyhedral projection in a conventional DGGS. We validate our method by creating three different 3D DGGSs tailored for three specific use cases. These use cases demonstrate our ability to quickly generate 3D global grids while achieving desired properties such as support for large ranges of altitudes, volume preservation between cells, and custom cell aspect ratio.

show abstract