# Big Data Integration in Remote Sensing across a Distributed Metadata-Based Spatial Infrastructure
## 4 Spatial Organization and Management of Remote Sensing Data
### LSI Organization Model of Multi-Source Remote Sensing Data  
The LSI organization model is based on the Geographical Coordinate Subdividing Grid with One Dimension Integer Coding on \(2^{n}\)Tree (GeoSOT) grid, which was proposed by the research group of Cheng around 2012 [34]. The main idea is expansion by three times for the latitude and longitude of Earth's surface. The first expansion is the original \(180^{\circ}\times 360^{\circ}\) Earth surface extended to \(512^{\circ}\times 512^{\circ}\); the expanded surface is viewed in level 0 grids, with grid code 0. Then, the level 0 grid is recursively partitioned quadrilaterally until reaching the \(1^{\circ}\) grid cell, with a total of nine subdivisions. The second expansion is processed for the \(1^{\circ}\) grid cell, namely, \(1^{\circ}\) extended to \(64^{\prime}\). The extended \(64^{\prime}\) grid cell is recursively partitioned quadrilaterally until reaching the \(1^{\prime}\) grid cell, with a total of 12 subdivisions. Similarly, the \(1^{\prime}\) grid cell is recursively partitioned quadrilaterally until reaching the \(\frac{1^{\prime\prime}}{2048}\) grid cell, with a total of 11 subdivisions.  
Finally, after the three expansions and 32 subdivisions, the system is used to cover the whole world, dividing the Earth into centimeter-level units using a hierarchy grid system with whole degrees, whole minutes, and whole seconds. Taking 32-bit quaternary coding at the Z-sequence, level subdivision cells were named as 00, 01, 02, 03, and so on; the location relationship of various spatial information products in different coordinate systems can be built with these globally unique and geographically meaningful codes [35; 36] (Figure 3).  
Based on the GeoSOT global segmentation strategy, the logical partition indexing code of each scene-based remote sensing data was calculated first in this paper. It is worth noting that there are three cases to consider regarding the logical code in general. Firstly, when the minimum bounding rectangle (MBR) [37] of a remote sensing image is completely contained in a GeoSOT grid, the logical partition index code is the corresponding GeoSOT grid code. Secondly, when the MBR of a remote sensing image spans two grids, the two grid codes will be the logical partition index codes. Thirdly, when the MBR of a remote sensing image spans four grids, the logical partition codes will be composed of the four codes [38] (Figure 4).  
After encoding each type of scene-based remote sensing data, then the virtual mapping between the logical partition indexing codes and position parameters (latitude and longitude) of each scene can be established easily (Figure 5). In fact, the logical partition indexing codes have become the form of spatial identification of each piece of scene-based remote sensing data when the virtual mapping is created. Reducing 8-index queries (latitude and longitude values of the upper, lower, left, and right four points) to no more than 4-index queries, the query speed increase in database systems is obvious. In addition, the logical partition indexing code of each scene center point is always used in the actual scene center point.  
Figure 4: Three cases with respect to the logical partition index code. MBR: minimum bounding rectangle.  
Figure 3: Level 0–3 Geographical Coordinate Subdividing Grid with One Dimension Integer Coding on \(2^{m}\)Tree (GeoSOT) grids.  
query process, and just one index query could be made in this situation. Therefore, based on the center point indexing code, a quick retrieval of massive remote sensing data can be realized.