Recent advancements in autonomous vehicle systems and computational capabilities have enhanced the effectiveness of simulation environments for testing and validating perception and control performance under diverse conditions. Most simulation engines, such as CARLA, utilize pre-existing city environments (e.g., Town01 to Town12). While these default 3D scenes meet basic autonomous vehicle testing requirements, there is increasing demand for more realistic maps representing various regions and scenarios. Existing methods for simulating real-world environments in these platforms are often time-consuming. Geospatial data converted to high-fidelity simulations from sources such as OpenStreetMap, Overture Maps, and other commercial providers frequently lack lane-level structures, elevation data, junction logic, and simulator-compatible semantic information.