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Connecting digital and physical representations through semantics and geometry
KTH, School of Architecture and the Built Environment (ABE), Real Estate and Construction Management, Geodesy and Satellite Positioning.ORCID iD: 0000-0001-9032-4305
2019 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The fields of geodesy and building information modeling (BIM) meet each other in the intersection between the physical and the digital world. Within the construction industry, the role of geodesy has typically been to describe the position of assets and to transform the geometries of those assets between coordinate systems suitable for design and coordinate systems with a known relation to the Earth. This is not changed by the introduction of BIM but rather emphasized by it, as higher degrees of automation and prefabrication increases the need for strict and non-distorting transformations. The objectoriented aspects of BIM require that captured geodata can be semantically classified and that objects can be reconstructed and extracted from the geodata. In this landscape, geodesy is the bridge between model and reality, connecting the two worlds through both semantics and geometry. This thesis is a comprehensive summary of three papers within these two topics. The first paper describes the geometric transformations required throughout the life cycle of a built asset and assesses the georeferencing capabilities of the open BIM standard Industry Foundation Classes (IFC). The second and third paper propose and showcase a methodology where image-based deep learning is used to extract roadside objects from mobile mapping data. The findings of the first paper include suggestions for how IFC can be improved in order to facilitate better georeferencing, and the second and third paper show that the proposed methodology performs well in comparison to a manual classification.

Abstract [sv]

De två områdena geodesi och byggnadsinformationsmodellering (BIM) möter varandra i skärningspunkten mellan den fysiska och den digitala världen. Inom byggindustrin har geodesins roll historiskt varit att positionsbestämma anläggningar samt att transformera deras geometrier mellan koordinatsystem lämpliga antingen för design eller för inmätning och utsättning. Detta har inte ändrats av att BIM börjat användas, utan det har snarare blivit ännu viktigare då högre nivåer av automatisering och prefabricering ställer högre krav på strikta och icke-deformerande transformationer. De objektorienterade aspekterna av BIM kräver att infångade geodata kan klassificeras semantiskt och att objekt kan återskapas och extraheras från dessa geodata. I detta landskap utgör geodesin en bro mellan modell och verklighet, och sammanlänkar dessa världar genom både semantik och geometri. Denna avhandling är en sammanfattning av tre artiklar inom dessa två områden. Den första artikeln beskriver de geometriska transformationer som krävs genom en anläggnings livscykel och utvärderar georefereringsförmågan hos den öppna BIM-standarden Industry Foundation Classes (IFC). Den andra och tredje artikeln föreslår och demonstrerar en metod där bildbaserad deep learning används för att extrahera vägnära objekt ur data insamlat genom mobile mapping. Slutsatserna från den första artikeln inkluderar förslag på hur IFC kan utvecklas för att möjliggöra bättre georeferering, och de två andra artiklarna visar att den föreslagna metoden presterar väl i jämförelse med en manuell klassificering.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2019. , p. 32
Series
TRITA-ABE-DLT ; 1914
National Category
Other Civil Engineering
Research subject
Geodesy and Geoinformatics
Identifiers
URN: urn:nbn:se:kth:diva-250333ISBN: 978-91-7873-196-1 (print)OAI: oai:DiVA.org:kth-250333DiVA, id: diva2:1307828
Presentation
2019-05-23, V3, Teknikringen 72, KTH, Stockholm, 10:00 (Swedish)
Opponent
Supervisors
Note

QC 20190429

Available from: 2019-04-29 Created: 2019-04-29 Last updated: 2019-04-29Bibliographically approved
List of papers
1. Geographic capabilities and limitations of Industry Foundation Classes
Open this publication in new window or tab >>Geographic capabilities and limitations of Industry Foundation Classes
2018 (English)In: Automation in Construction, ISSN 0926-5805, E-ISSN 1872-7891, Vol. 96, p. 554-566Article in journal (Refereed) Published
Abstract [en]

Infrastructure design is conducted in a 3D Cartesian coordinate system with the assumption that the Earth is flat and that the scale is constant over the entire project area. Map projections are commonly used to georeference the designed geometries before constructing them on the surface of the Earth. The scale in a map projection varies depending on the position in the map plane, which leads to scale distortions between the designed geometries and the geometries staked out for construction. These distortions are exaggerated for large longitudinal projects such as the construction of roads and railroads because the construction site spans a larger area. Building Information Modeling (BIM) is increasing in popularity as a way to manage information within a construction project. Its use is more widespread in the building industry, but it is currently being adopted by the infrastructure industry as well. The open BIM standard IFC (Industry Foundation Classes) has recently developed support for alignment geometries, and full support for disciplines such as road and railroad construction is underway. This study tests whether the current IFC standard can facilitate georeferencing with sufficiently low distortion for the construction of infrastructure. This is done by performing georeferencing using three different methods, all using the information provided in the IFC schema, and by calculating the scale distortions caused by the different methods. It is concluded that the geographic capabilities of the IFC schema could be improved by adding a separate scale factor for the horizontal plane and support for object-specific map projections.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
Georeferencing, BIM, IFC
National Category
Construction Management
Identifiers
urn:nbn:se:kth:diva-240760 (URN)10.1016/j.autcon.2018.10.014 (DOI)000452345800042 ()2-s2.0-85055585349 (Scopus ID)
Funder
Swedish Transport Administration, FUD 6240 FUD 6240
Note

QC 20190107

Available from: 2019-01-07 Created: 2019-01-07 Last updated: 2019-04-29Bibliographically approved
2. Classification and object reconstruction in point clouds using semantic segmentation and transfer learning
Open this publication in new window or tab >>Classification and object reconstruction in point clouds using semantic segmentation and transfer learning
2019 (English)Conference paper, Published paper (Refereed)
National Category
Other Civil Engineering
Identifiers
urn:nbn:se:kth:diva-250331 (URN)
Conference
CIB World Building Congress 2019
Note

QC 20190429

Available from: 2019-04-29 Created: 2019-04-29 Last updated: 2019-04-29Bibliographically approved
3. Automatic extraction of roadside objects from mobile mapping data
Open this publication in new window or tab >>Automatic extraction of roadside objects from mobile mapping data
2019 (English)In: Article in journal (Refereed) Submitted
National Category
Other Civil Engineering
Identifiers
urn:nbn:se:kth:diva-250332 (URN)
Note

QC 20190429

Available from: 2019-04-29 Created: 2019-04-29 Last updated: 2019-04-29Bibliographically approved

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