RT Journal Article
T1 Automatic classification of urban ground elements from mobile laser scanning data
A1 Balado Frías, Jesús
A1 Díaz Vilariño, Lucía
A1 Arias Sánchez, Pedro
A1 González Jorge, Higinio
K1 330522 Metrología de la edificación
K1 331102 Ingeniería de control
K1 330534 Topografía de la edificación
AB Accessibility diagnosis of as-built urban environments is essential for path planning, especially in case of people with reduced mobility and it requires an in-depth knowledge of ground elements. In this paper, we present a new approach for automatically detect and classify urban ground elements from 3D point clouds. The methodology enables a high level of detail classification from the combination of geometric and topological information. The method starts by a planar segmentation followed by a refinement based on split and merge operations. Next, a feature analysis and a geometric decision tree are followed to classify regions in preliminary classes. Finally, adjacency is studied to verify and correct the preliminary classification based on a comparison with a topological graph library. The methodology is tested in four real complex case studies acquired with a Mobile Laser Scanner Device. In total, five classes are considered (roads, sidewalks, treads, risers and curbs). Results show a success rate of 97% in point classification, enough to analyse extensive urban areas from an accessibility point of view. The combination of topology and geometry improves a 10% to 20% the success rate obtained with only the use of geometry.
PB Automation in Construction
SN 09265805
YR 2018
FD 2018-02
LK http://hdl.handle.net/11093/1350
UL http://hdl.handle.net/11093/1350
LA eng
NO Automation in Construction, 86, 226-239 (2018)
NO Cátedra SCI-EYSA. Smart cities e seguridade vial
DS Investigo
RD 06-dic-2023