The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XL-4/W2
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-4/W2, 51–56, 2013
https://doi.org/10.5194/isprsarchives-XL-4-W2-51-2013
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-4/W2, 51–56, 2013
https://doi.org/10.5194/isprsarchives-XL-4-W2-51-2013

  25 Oct 2013

25 Oct 2013

Spatial Big Data Organization, Access and Visualization with ESSG

L. X. Wu1,2,3, J. Q. Yu1, Y. Z. Yang4, and Y. J. Jia1 L. X. Wu et al.
  • 1School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, China
  • 2Academy of Disaster Reduction and Emergency Management, Beijing Normal University, Beijing, China
  • 3IoT (Mine Perception) Center, China University of Mining and Technology, Xuzhou, China
  • 4Institute of Geo-informatics and Digital Mine Research, Northeastern University, Shenyang, China

Keywords: Spatial big data, Global Spatial Grid, ESSG, Spatial Infrastructure, Visualization, Mapping, Internet/Web, Cloud

Abstract. There are hundreds of spatial reference frame (SRF) being applied, and the great difference among SRFs has blocked the share of global data on planet Earth. A conceptual spheroid of radius 12,800 km and a spheroid degenerated octree grid method are applied to produce an earth system spatial grid (ESSG), which is of natural characteristics to be applied as a new common SRF. A triple CTA is designed as ESSG-based data structure to organize the big data of planet Earth, and a 2D table of a unique label and limitless records for time slices and attribute values is present to record the data of each grid. The big data on planet Earth can hence be gridded and interrelated without discipline gaps and SRF obstacles. An integral data organization mode is designed, and three potential routes are presented for users to access shareable global data in cloud environment. Furthermore, with global crust, atmosphere, DEM, and satellite image being examples, the integrated visualization of global large objects is demonstrated.