Octree-based indexing for 3D pointclouds within an Oracle Spatial DBMS

A large proportion of today's digital datasets have a spatial component. The effective storage and management of which poses particular challenges, especially with light detection and ranging (LiDAR), where datasets of even small geographic areas may contain several hundred million points. While in the last decade 2.5-dimensional data were prevalent, true 3-dimensional data are increasingly commonplace via LiDAR. They have gained particular popularity for urban applications including generation of city-scale maps, baseline data disaster management, and utility planning. Additionally, LiDAR is commonly used for flood plane identification, coastal-erosion tracking, and forest biomass mapping. Despite growing data availability, current spatial information systems do not provide suitable full support for the data's true 3D nature. Consequently, one system is needed to store the data and another for its processing, thereby necessitating format transformations. The work presented herein aims at a more cost-effective way for managing 3D LiDAR data that allows for storage and manipulation within a single system by enabling a new index within existing spatial database management technology. Implementation of an octree index for 3D LiDAR data atop Oracle Spatial 11g is presented, along with an evaluation showing up to an eight-fold improvement compared to the native Oracle R-tree index.

► Current spatial information systems do not provide adequate support for LiDAR.
► Multiple systems and software are needed resulting in complexity and inaccuracy.
► An octree in a spatial database management system overcomes these problems.
► This paper benchmarks a new solution to effectively managing LiDAR data.
► This is done within an existing commercial system.