Keywords:retrofitting potential, LCA, point cloud, semantic enrichment
To meet the climate goals of the Paris agreement, the focus on energy efficiency needs to be shifted to increase the retrofitting rate of the existing building stock. Due to the lack of usable information on the existing building stock, reasoning about the retrofitting potential in early design stages is difficult. Therefore, deconstructing and building new is often regarded as the more reliable and economical option. Digital methods are missing or not robust enough to capture and reconstruct digital models of existing buildings efficiently and automatically derive reliable decision-support about whether demolition and new construction or retrofitting of existing buildings is more suitable. This paper proposes a robust, automated method for calculating existing buildings' life cycle assessments (LCA) using point clouds as input data. The main focus lies in bridging the gap between point clouds and importing semantic 3D models for LCA calculation. Therefore, the automation steps include a geometric transformation from point cloud to 3D surface model, followed by a semantic classification of the surfaces to thermal layers and their materials by assuming the surface elements by building age class.