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Model and Reality

Connecting BIM and the Built Environment

Time: Fri 2021-06-04 09.00

Location: Videolänk, Du som saknar dator /datorvana kontakta Milan Horemuz / Use the e-mail address if you need technical assistance, Stockholm (English)

Subject area: Geodesy and Geoinformatics, Geodesy

Doctoral student: Gustaf Uggla , Geodesi och satellitpositionering

Opponent: Professor Alojz Kopáčik, Slovak University of Technology

Supervisor: Docent Milan Horemuz, Geodesi och satellitpositionering; PhD Patric Jansson, Trafikverket; Professor emeritus Väino Tarandi, Fastigheter och byggande

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The adoption of building information modeling (BIM) in the architecture, engineering, and construction (AEC) industry is changing the way informa-tion regarding the built environment is created, stored, and exchanged. In short, documents are replaced with databases, processes are automated, and timelines become more circular with an emphasis on managing the life cycles of all manufactured objects. This has both direct and indirect consequences for the fields of geodesy and geographic information. Although geodesy and surveying have played a vital role in the construction process for a long time, new data standards and higher degrees of prefabrication and automation in the actual construction means that the topic of georeferencing must be revisited. In addition, using object oriented data structures means that semantic information must be inferred from geodata such as point clouds and images in order to adequately document existing assets. This thesis addresses the handling of 3D spatial information by analyzing different georeferencing methods and metadata used to describe the quality and characteristics of geodata.The outcomes include a recommendation for how the open BIM standard Industry Foundation Classes (IFC) could be extended to support more robust georeferencing, a suggestion that all standards and exchange formats used forthe built environment should include metadata for tolerance and uncertainty, and a framework that can describe characteristics of 3D spatial data that are not covered by conventional geographic metadata. On the semantic side, this thesis proposes an image-based method for identifying roadside objects in mobile laser scanning (MLS) point clouds, and it also explores the possibilities to train neural networks for point cloud segmentation by creating training data from 3D mesh models used in infrastructure design. Overall, the thesis describes the connection between model and reality, the importance of geodesy and geodetic surveying in this context, and makes contributions to both the geometric and semantic aspects of modeling the built environment.