Urban solar energy assessments are vital for developing solar cadastres, enabling optimized renewable energy integration in cities. This study evaluates the effectiveness of different interpolation techniques for generating Digital Surface Models (DSMs) from LiDAR data to estimate rooftop solar potential on three building rooftops of the HBKU Research Complex in Education City, Qatar Foundation. High-resolution LiDAR point cloud data, captured at approximately 3.06 pts./m2 resolution, was processed using QGIS to create DSMs of a fixed resolution of 0.5 m and the UMEP-SEBE plugin was used to obtain solar potential on the DSM. Four interpolation methods were tested to create the DSMs: Triangulated Irregular Network (TIN), Inverse Distance Weighting (IDW), Natural Neighbor (NN), and Radial Basis Function-Thin Plate Spline (RBF-TPS). The study shows that interpolation techniques do affect solar potential estimation. TIN emerged as the most effective method, preserving edge details and height accuracy. To validate the DSM rooftop visual of TIN it was compared with Google Earth rooftop area measurements, this showed TIN’s superior accuracy with percentage errors of 5.671%, 11.192%, and 2.127% for three selected buildings. Solar energy potential assessments using TIN yielded mean annual average values of 1884.491 kWh/m2, 1931.027 kWh/m2, and 1873.752 kWh/m2, respectively. Among alternative methods, IDW closely approximated TIN’s results, followed by NN and RBF-TPS. This study highlights TIN’s suitability for generating accurate DSMs and solar potential assessment for rooftops in the context of solar cadastre development, providing a robust basis for urban renewable energy planning. Future research will explore higher DSM resolution, facade-based evaluations and additional method comparisons to further enhance solar energy assessments.