Efficient Encoding and Decoding Extended Geocodes for Massive Point Cloud Data

Abstract

With the development of mobile surveying and mapping technologies, point cloud data has been emerging in a variety of applications including robot navigation, self-driving drones/vehicles, and three-dimensional (3D) urban space modeling. In addition, there is an increasing demand for the database management system to share and reuse point cloud data, unlike being treated as archive files in the traditional uses and applications. However, database scalability needs to be explored to process and manage a massive volume of point cloud data defined by a 3D (X, Y, and Z) coordinates system.  The typical approach to handle big data and distribute it across multiple nodes is data partitioning.~Geohashing is a popular way to convert a latitude/longitude spatial point into a code/string and has used for storing data into buckets of the grid. Many methods of handling big geospatial data, especially NoSQL databases, are based on the geohashing techniques. In this paper, we propose an efficient method to encode/decode 3D point cloud in a Discrete Global Grid System (DGGS) that represents the Earth as hierarchical sequences of equal area/volume tessellations, similar to geohash. The current geohash of base36 has the difficulties of working with high-resolution 3D point clouds for data storage, filter, integration, and analytics because of its limitation of cell size and unequal areas. We employ DGGS-based Morton codes with more than 64 bits for precise 3D coordinates of point cloud and compare the encoding/decoding performance between two implementations: using strings and using the combination of bit interleaving and lookup tables.

A Blocky and Layered Management Schema for Remote Sensing Data

“…Sahr and White [7] and Kimerling et al [8] further improved the grid types and modeling rules of DGGS. Subsequently, domestic and foreign researchers successively proposed a variety of global segmentation models, such as tetrahedron [9], hexagon [10], octahedron [11] and icosahedron [12] models, and these models have been widely applied in many fields based on polyhedral structures, such as vector data structures [13], point cloud data [14], spatial data indexes [15], [16], data archiving and distribution [17], and global dynamic data structures [18]. DGGS is used by Wolfe et al [19] and Shelock et al [20] to manage Land-sat8 and MODIS data, which proves the value of the grid data organization scheme for multiple spatial observation data storage and analysis.…”