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  • 1.
    Uggla, Gustaf
    KTH, School of Architecture and the Built Environment (ABE), Real Estate and Construction Management, Geodesy and Satellite Positioning.
    Automatic extraction of roadside objects from mobile mapping data2019In: Article in journal (Refereed)
  • 2.
    Uggla, Gustaf
    KTH, School of Architecture and the Built Environment (ABE), Real Estate and Construction Management, Geodesy and Satellite Positioning.
    Classification and object reconstruction in point clouds using semantic segmentation and transfer learning2019Conference paper (Refereed)
  • 3.
    Uggla, Gustaf
    KTH, School of Architecture and the Built Environment (ABE), Real Estate and Construction Management, Geodesy and Satellite Positioning.
    Connecting digital and physical representations through semantics and geometry2019Licentiate 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.

  • 4.
    Uggla, Gustaf
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Real Estate and Construction Management, Geodesy and Satellite Positioning.
    Horemuz, Milan
    KTH, School of Architecture and the Built Environment (ABE), Real Estate and Construction Management, Geodesy and Satellite Positioning.
    Geographic capabilities and limitations of Industry Foundation Classes2018In: Automation in Construction, ISSN 0926-5805, E-ISSN 1872-7891, Vol. 96, p. 554-566Article in journal (Refereed)
    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.

  • 5.
    Uggla, Gustaf
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Real Estate and Construction Management, Geodesy and Satellite Positioning.
    Horemuz, Milan
    KTH, School of Architecture and the Built Environment (ABE), Real Estate and Construction Management, Geodesy and Satellite Positioning.
    Georeferencing Methods for IFC2018In: Proceedings - 2018 Baltic Geodetic Congress, BGC-Geomatics 2018, Institute of Electrical and Electronics Engineers Inc. , 2018, p. 207-211Conference paper (Refereed)
    Abstract [en]

    Building Information Modelling (BIM) is becoming a standard tool for information management throughout the life cycle of a construction project. Elements in BIM are designed in a Cartesian coordinate system (Engineering system) with no direct relation to the project's geographic location. Accurate georeferencing of BIM data is required both for construction and integration with Geographic Information Systems (GIS), as improperly treated or neglected scale distortions can lead to costly delays in construction as problems requiring ad hoc solutions may arise on site Industry Foundation Classes (IFC) is an open BIM standard developed by buildingSMART, and the current version IFC 4 has recently been extended with IFC Alignment, which includes support for alignment geometries used for infrastructure design. This paper investigates the geographic capabilities of IFC 4 and its extension IFC Alignment. The study identifies a lack of support for non-uniform scale factors and object-specific map projections as the largest weaknesses.

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