This thesis is dedicated to contributing to the field of semi-automatic extraction of 3D buildings in level of detail 2 (LOD2). The data used in the thesis is open data available in Sweden, such as building footprints from local municipalities and national light detection and ranging (LiDAR) data. The study site is a part of a central street of the city Gävle, Sweden. To perform the automatic extraction of 3D buildings the software ArcGIS PRO and its solution for 3D buildings was used. The method consists of experimenting with the segmentation of the building footprints and then comparing the resulting models RMSE as a whole and for two specific buildings of different size (one small and one large building). There are some improvements that can be made in the quality assessment of the 3D models as the method used in this thesis bases the RMSE of the 3D models the obtained digital surface model (DSM). One possible way to improve the quality assessment would have been to perform a field survey to gather reference points with lower uncertainties than the DSM e.g. employing a total station. The conclusions of the thesis identify key settings that affects the 3D modelling. The best model, lowest RMSE, achieved an RMSE of 2.36 meters with tuning the setting of spatial detailwhile the rest of the settings were kept at their default values. Furthermore, the thesis identifies that the regularization tolerance setting, when segmenting the building footprints, affects the 3D models of large buildings negatively, as seen in an increase of RMSE when it is set to 0.5 meters and lower. However, further studies are required on a larger scale to better validate the conclusions.