Using densification potentials for urban development is one of the current challenges in urban planning. An important prerequisite is an up-to-date GIS data basis, which contains potential densification sites. Existing data is often based on manual acquisition, which is time-consuming and costly. Therefore, the value of such data depends on the effort of creating and updating it, which can be improved by automatic data collection. We developed an ML-supported pipeline containing a deep learning model and 3D point cloud processing. This pipeline addresses two important densification potentials: infill sites and vertical extension. Integrating the automated processing into QGIS as a plugin provides users an easy-to-use tool to generate a data basis for densification potentials for their decision-making process. We evaluate the effectiveness of our approach using real-world data from two different cities. The results show its usability in practice and the possibility of transferring it to different locations.