TY - JOUR
T1 - Octree-based indexing for 3D pointclouds within an Oracle Spatial DBMS
AU - Schön, Bianca
AU - Mosa, Abu Saleh Mohammad
AU - Laefer, Debra F.
AU - Bertolotto, Michela
PY - 2013/2
Y1 - 2013/2
N2 - 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.
AB - 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.
KW - Laser scanning
KW - LiDAR pointcloud data
KW - Octree
KW - Spatial databases
KW - Three-dimensional indexing
UR - http://www.scopus.com/inward/record.url?scp=84871940015&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84871940015&partnerID=8YFLogxK
U2 - 10.1016/j.cageo.2012.08.021
DO - 10.1016/j.cageo.2012.08.021
M3 - Article
AN - SCOPUS:84871940015
SN - 0098-3004
VL - 51
SP - 430
EP - 438
JO - Computers and Geosciences
JF - Computers and Geosciences
ER -