The demand for accurate, lightweight 3D building models is rapidly growing in urban analysis, digital twins, and geospatial information systems. Single-source airborne point clouds, such as airborne laser scanning (ALS) or dense image matching (DIM), often suffer from geometric incompleteness, uneven density, and misalignments, limiting the reliability of Level of Detail (LOD) building reconstructions. While substantial progress has been made in single-source building reconstruction and multi-source fusion, fully automated LOD generation pipelines that effectively exploit cross-source airborne data remain limited. This paper presents an automated workflow for generating precise LOD building models from cross-source fused point clouds, leveraging the precision of ALS and the high resolution of DIM to improve model fidelity. Using point clouds obtained from a slice-to-slice fusion approach, experiments on Luxembourg datasets demonstrate a reduced model standard deviation of 0.17m compared to 0.20m for ALS, 0.29m for DIM, and 0.27m for conventional ICP-based fused point clouds. The results show that our workflow, combined with a polygon fitting algorithm and cross-source fused data, significantly enhances building model accuracy and geometric completeness, highlighting the value of multi-source integration for automated 3D city modeling.