Automatic classification of urban ground elements from mobile laser scanning data
IDENTIFICADOR UNIVERSAL: http://hdl.handle.net/11093/1350
VERSIÓN EDITADA: https://linkinghub.elsevier.com/retrieve/pii/S092658051730153X
MATERIA UNESCO: 330522 Metrología de la edificación ; 331102 Ingeniería de control ; 330534 Topografía de la edificación
TIPO DE DOCUMENTO: article
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.
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