Urban environments are increasingly shaped by advanced digital technologies and demand dynamic, data-rich platforms for managing complex city systems. Urban Digital Twins (UDTs) enable comprehensive analysis and intelligent decision-making but face challenges integrating diverse geospatial data like Mobile Laser Scanning (MLS) point clouds, CityGML models, and Digital Terrain Models (DTM) due to differences in data type, precision, and semantics. Overcoming these inconsistencies is essential for accurate and actionable urban representations. In this thesis, a robust framework for the systematic integration of heterogeneous 3D geospatial data like (MLS) point clouds, CityGML LOD2 models, and DTMs into a unified voxel-based UDT was developed. The primary objective was to enhance geometric accuracy and semantic richness using a voxel-based integration approach to overcome inconsistencies in default data formats. The methodology involved semi-automated pipelines for aligning MLS with CityGML, refining DTMs using MLS and CityGML, and integrating all datasets into a voxel structure within a spatial database (PostGIS). Experiments on a test area in Hannover demonstrated improved positional accuracy after alignment, enhanced terrain models, and the preservation of geometric and semantic information within the integrated voxel UDT, enabling multi-source 3D urban analysis and visualization.
