A Knowledge Base for Automatic Feature Recognition from Point Clouds in an Urban Scene
AbstractLiDAR technology can provide very detailed and highly accurate geospatial information on an urban scene for the creation of Virtual Geographic Environments (VGEs) for different applications. However, automatic 3D modeling and feature recognition from LiDAR point clouds are very complex tasks. This becomes even more complex when the data is incomplete (occlusion problem) or uncertain. In this paper, we propose to build a knowledge base comprising of ontology and semantic rules aiming at automatic feature recognition from point clouds in support of 3D modeling. First, several modules for ontology are defined from different perspectives to describe an urban scene. For instance, the spatial relations module allows the formalized representation of possible topological relations extracted from point clouds. Then, a knowledge base is proposed that contains different concepts, their properties and their relations, together with constraints and semantic rules. Then, instances and their specific relations form an urban scene and are added to the knowledge base as facts. Based on the knowledge and semantic rules, a reasoning process is carried out to extract semantic features of the objects and their components in the urban scene. Finally, several experiments are presented to show the validity of our approach to recognize different semantic features of buildings from LiDAR point clouds. View Full-Text
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Xing, X.-F.; Mostafavi, M.-A.; Chavoshi, S.H. A Knowledge Base for Automatic Feature Recognition from Point Clouds in an Urban Scene. ISPRS Int. J. Geo-Inf. 2018, 7, 28.
Xing X-F, Mostafavi M-A, Chavoshi SH. A Knowledge Base for Automatic Feature Recognition from Point Clouds in an Urban Scene. ISPRS International Journal of Geo-Information. 2018; 7(1):28.Chicago/Turabian Style
Xing, Xu-Feng; Mostafavi, Mir-Abolfazl; Chavoshi, Seyed H. 2018. "A Knowledge Base for Automatic Feature Recognition from Point Clouds in an Urban Scene." ISPRS Int. J. Geo-Inf. 7, no. 1: 28.
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