The increasing availability of large-scale airborne LiDAR (Light Detection And Ranging) data related to urban scenarios requires the development of methods for transforming raw point clouds into structured 3D urban representations. The ability to generate accurate representations starting from raw data meets fundamental requirements in the fields of urban visualization, interactive simulation, and digital twins, providing a solid foundation for graphics and immersive reality applications. In this work, we present LiD2LOD, a framework for the automatic generation of Level of Detail 1 (LOD1) city models from LiDAR point clouds. Our tool can create both semantic models according to the CityGML standard, suitable for geospatial data integration, and lightweight triangular meshes, optimized for visualization and rendering. We test our approach on point clouds that represent historical cities characterized by complex morphology, thereby proving its scalability and robustness.