Automated digital building modelling from heterogeneous as-built data taking into account their quality characteristics - ADIBAMOD-Q

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The goal of the project is to develop methods for largely automated generation of digital building models from heterogeneous as-built data for parametric associative building information modelling (BIM model). One of the challenges in developing automated BIM generation is the inclusion of heterogeneous data from different sources. As-built or measured data includes, for example, images from photogrammetric surveys, 3D point clouds from measurements with laser scanners (LIDAR data), existing 2D and 3D CAD models or 2D site plans. It is thus clear that for the determination of geometric units for the creation of BIM models, data sources with different error levels have to be integrated. The integration of different data types with individual stochastic properties is a key task in many geodetic issues. Frequently, measurement data from different sources are used for the determination of geometric parameters with the help of a least squares adjustment. Algorithmic approaches for determining variances of individual data groups, robust solution strategies for identifying erroneous data or regularised solutions are established procedures in geodesy. These mathematical approaches are to be extended for the processing of heterogeneous information and measurement data for building modelling, whereby the stochastic information is to be used for conclusions in the further modelling process. The proposed quality infrastructure for building modelling based on 3D point clouds, taking into account partly inaccurate, incomplete or even faulty as-built data, is to provide an IFC-compliant BIM model as the final result. The Industry Foundation Classes (IFC) are an open standard in the building and construction industry for the digital description of building models.