In order to meet the climate goals of the Paris agreement, the focus of energy efficiency needs to be shifted to increasing the renovation rate of the existing building stock. Due to the lack of usable information on the existing building stock, reasoning about the renovation potential in early design stages is difficult. Therefore, deconstructing and building new is often regarded as the more reliable and economical option. Functional methods to efficiently capture and reconstruct digital models of existing buildings are missing. Based on this, it is currently not possible to automatically derive a reliable decision support about whether demolition and new construction or renovation of existing buildings are more suitable. This master thesis aims to propose a robust, automated method for calculating life cycle assessments (LCA) of existing buildings by using point clouds as input data. The main focus lies on bridging the gap between point clouds and the import of semantic 3D models for LCA calculation. Therefore, the automation steps include a geometric reconstruction from point cloud to 3D surface model, followed by a semantic categorisation of the surfaces to thermal classes and their materials by assuming the building age class. Furthermore, the window-to-wall ratio is determined using intensity features from the laser scan recording of the point cloud.     «

In order to meet the climate goals of the Paris agreement, the focus of energy efficiency needs to be shifted to increasing the renovation rate of the existing building stock. Due to the lack of usable information on the existing building stock, reasoning about the renovation potential in early design stages is difficult. Therefore, deconstructing and building new is often regarded as the more reliable and economical option. Functional methods to efficiently capture and reconstruct digital model...     »