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3D Point cloud to BIM: semi-automated framework to define IFC alignment entities from MLS-acquired LiDAR data of highway roads

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Show simple item record Soilán Rodríguez, Mario Justo Dominguez, Andrés Sánchez Rodríguez, Ana Riveiro Rodríguez, Belén 2021-02-01T07:54:48Z 2021-02-01T07:54:48Z 2020-07-17
dc.identifier.citation Remote Sensing, 12(14): 2301 (2020) spa
dc.identifier.issn 20724292
dc.description.abstract Building information modeling (BIM) is a process that has shown great potential in the building industry, but it has not reached the same level of maturity for transportation infrastructure. There is a standardization need for information exchange and management processes in the infrastructure that integrates BIM and Geographic Information Systems (GIS). Currently, the Industry Foundation Classes standard has harmonized different infrastructures under the Industry Foundation Classes (IFC) 4.3 release. Furthermore, the usage of remote sensing technologies such as laser scanning for infrastructure monitoring is becoming more common. This paper presents a semi-automated framework that takes as input a raw point cloud from a mobile mapping system, and outputs an IFC-compliant file that models the alignment and the centreline of each road lane in a highway road. The point cloud processing methodology is validated for two of its key steps, namely road marking processing and alignment and road line extraction, and a UML diagram is designed for the definition of the alignment entity from the point cloud data. spa
dc.description.sponsorship Horizon 2020 Framework Programme | Ref. 769255 spa
dc.description.sponsorship Ministerio de Ciencia, Innovación y Universidades | Ref. RTI2018-095893-B-C21 spa
dc.description.sponsorship Ministerio de Ciencia e Innovación y Universidades | Ref. FJC2018-035550-I spa
dc.language.iso eng spa
dc.publisher Remote Sensing spa
dc.rights Creative Commons Attribution (CC BY) license
dc.rights.uri (
dc.title 3D Point cloud to BIM: semi-automated framework to define IFC alignment entities from MLS-acquired LiDAR data of highway roads spa
dc.type article spa
dc.rights.accessRights openAccess spa
dc.identifier.doi 10.3390/rs12142301
dc.identifier.editor spa
dc.publisher.departamento Enxeñaría dos recursos naturais e medio ambiente spa
dc.publisher.departamento Enxeñaría dos materiais, mecánica aplicada e construción spa
dc.publisher.grupoinvestigacion Xeotecnoloxías Aplicadas spa
dc.subject.unesco 3311.02 Ingeniería de Control spa
dc.subject.unesco 3305.06 Ingeniería Civil spa
dc.subject.unesco 3307.07 Dispositivos láser spa 2021-01-27T11:25:19Z

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