This study presents a semi-automated Level of Detail (LoD) 2 building modelling and analysis framework, implemented using fully open-source geographic information system (GIS) software, for the high-accuracy identification of rooftop photovoltaic (PV) potential in smart city digital twins. The LoD 1 building model, traditionally based on two-dimensional building footprint data used in large-scale building modelling, systematically overestimates solar energy potential as it neglects roof pitch, orientation and shading from the immediate surroundings. In this study, analyses were conducted in a high-density urban area of Birmingham, UK, using 1-metre resolution aerial LiDAR point clouds provided by the UK Department for Environment, Food and Rural Affairs (DEFRA). Throughout the process, reliance on proprietary software was completely eliminated; point cloud pre-processing, building boundary extraction using DBSCAN clustering and Concave Hull algorithms, and orthogonalisation filters were carried out entirely within the QGIS environment. Dynamic solar radiation simulations were performed using the SAGA ‘Potential Incoming Solar Radiation’ algorithm. The findings of the study provide a scalable framework for sustainable urban planning by enhancing LoD of modelled buildings and laying the groundwork for the semantic enrichment of urban digital twins. Furthermore, it generates outputs that provide concrete support to end-users, thereby helping achieve global net-zero targets.