Point cloud voxel classification of aerial urban LiDAR using voxel attributes and random forest approach

Aljumaily, Harith; Laefer, Debra F.; Cuadra Fernández, María Dolores; Velasco de Diego, Manuel

Publication:
Point cloud voxel classification of aerial urban LiDAR using voxel attributes and random forest approach

dc.affiliation.dpto	UC3M. Departamento de Informática	es
dc.affiliation.grupoinv	UC3M. Grupo de Investigación: Human Language and Accessibility Technologies (HULAT)	es
dc.contributor.author	Aljumaily, Harith
dc.contributor.author	Laefer, Debra F.
dc.contributor.author	Cuadra Fernández, María Dolores
dc.contributor.author	Velasco de Diego, Manuel
dc.date.accessioned	2023-11-23T11:27:57Z
dc.date.available	2023-11-23T11:27:57Z
dc.date.issued	2023-04-01
dc.description.abstract	The opportunities now afforded by increasingly available, dense, aerial urban LiDAR point clouds (greater than100 pts/m2) are arguably stymied by their sheer size, which precludes the effective use of many tools designed for point cloud data mining and classification. This paper introduces the point cloud voxel classification (PCVC) method, an automated, two-step solution for classifying terabytes of data without overwhelming the computational infrastructure. First, the point cloud is voxelized to reduce the number of points needed to be processed sequentially. Next, descriptive voxel attributes are assigned to aid in further classification. These attributes describe the point distribution within each voxel and the voxel's geo-location. These include 5 point-descriptors (density, standard deviation, clustered points, fitted plane, and plane's angle) and 2 voxel position attributes (elevation and neighbors). A random forest algorithm is then used for final classification of the object within each voxel using four categories: ground, roof, wall, and vegetation. The proposed approach was evaluated using a 297,126,417 point dataset from a 1 km2 area in Dublin, Ireland and 50% denser dataset of New York City of 13,912,692 points (150 m2). PCVC's main advantage is scalability achieved through a 99 % reduction in the number of points that needed to be sequentially categorized. Additionally, PCVC demonstrated strong classification results (precision of 0.92, recall of 0.91, and F1-score of 0.92) compared to previous work on the same data set (precision of 0.82-0.91, recall 0.86-0.89, and F1-score of 0.85-0.90).	en
dc.description.sponsorship	This work was funded by the National Science Foundation award 1940145.	en
dc.identifier.bibliographicCitation	Aljumaily, H., Laefer, D.F., Cuadra, D., Velasco, M. Point cloud voxel classification of aerial urban LiDAR using voxel attributes and random forest approach. International Journal of Applied Earth Observation and Geoinformation, 118 (2023) 103208. doi:10.1016/j.jag.2023.103208	en
dc.identifier.doi	https://doi.org/10.1016/j.jag.2023.103208
dc.identifier.issn	1569-8432
dc.identifier.publicationtitle	International Journal of Applied Earth Observation and Geoinformation	en
dc.identifier.publicationvolume	118
dc.identifier.uri	https://hdl.handle.net/10016/38938
dc.identifier.uxxi	AR/0000033504
dc.language.iso	eng
dc.publisher	Elservier	en
dc.rights	© 2023 TheAuthors	en
dc.rights	Atribución-NoComercial-SinDerivadas 3.0 España	*
dc.rights.accessRights	open access	en
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.eciencia	Informática	es
dc.subject.other	big data	en
dc.subject.other	Dbscan algorithm	en
dc.subject.other	lidar	en
dc.subject.other	mapreduce	en
dc.subject.other	object classification	en
dc.subject.other	Ransac algorithm	e
dc.title	Point cloud voxel classification of aerial urban LiDAR using voxel attributes and random forest approach	en
dc.type	research article	*
dc.type.hasVersion	VoR	*
dspace.entity.type	Publication