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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

Identificadores del recurso

Remote Sensing, 12(14): 2301 (2020)

20724292

http://hdl.handle.net/11093/1718

10.3390/rs12142301

https://www.mdpi.com/2072-4292/12/14/2301

Procedencia

Ficha

Título:: 3D Point cloud to BIM: semi-automated framework to define IFC alignment entities from MLS-acquired LiDAR data of highway roads
Tema:: 3311.02 Ingeniería de Control; 3305.06 Ingeniería Civil; 3307.07 Dispositivos láser
Descripción:: 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.; Ministerio de Ciencia, Innovación y Universidades | Ref. RTI2018-095893-B-C21; Ministerio de Ciencia e Innovación y Universidades | Ref. FJC2018-035550-I
Idioma:: English
Relación:: info:eu-repo/grantAgreement/EC/H2020/769255
Autor/Productor:: Soilán Rodríguez, Mario; Justo Dominguez, Andrés; Sánchez Rodríguez, Ana; Riveiro Rodríguez, Belén
Editor:: Remote Sensing; Enxeñaría dos recursos naturais e medio ambiente; Enxeñaría dos materiais, mecánica aplicada e construción; Xeotecnoloxías Aplicadas
Derechos:: Attribution 4.0 International; https://creativecommons.org/licenses/by/4.0/; openAccess
Fecha:: 2021-02-01T07:54:48Z; 2020-07-17; 2021-01-27T11:25:19Z
Tipo de recurso:: article

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1. <mods:name>
  1. <mods:namePart>Soilán Rodríguez, Mario</mods:namePart>
  </mods:name>
2. <mods:name>
  1. <mods:namePart>Justo Dominguez, Andrés</mods:namePart>
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3. <mods:name>
  1. <mods:namePart>Sánchez Rodríguez, Ana</mods:namePart>
  </mods:name>
4. <mods:name>
  1. <mods:namePart>Riveiro Rodríguez, Belén</mods:namePart>
  </mods:name>
5. <mods:extension>
  1. <mods:dateAvailable encoding="iso8601">2021-02-01T07:54:48Z</mods:dateAvailable>
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6. <mods:extension>
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  1. <mods:dateIssued encoding="iso8601">2020-07-17</mods:dateIssued>
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8. <mods:identifier type="citation">Remote Sensing, 12(14): 2301 (2020)</mods:identifier>
9. <mods:identifier type="issn">20724292</mods:identifier>
10. <mods:identifier type="uri">http://hdl.handle.net/11093/1718</mods:identifier>
11. <mods:identifier type="doi">10.3390/rs12142301</mods:identifier>
12. <mods:identifier type="editor">https://www.mdpi.com/2072-4292/12/14/2301</mods:identifier>
13. <mods: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.</mods:abstract>
14. <mods:language>
  1. <mods:languageTerm>eng</mods:languageTerm>
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16. <mods:accessCondition type="useAndReproduction">openAccess</mods:accessCondition>
17. <mods:accessCondition type="useAndReproduction">Attribution 4.0 International</mods:accessCondition>
18. <mods:titleInfo>
  1. <mods:title>3D Point cloud to BIM: semi-automated framework to define IFC alignment entities from MLS-acquired LiDAR data of highway roads</mods:title>
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