As the urban digital transformation continues to advance, virtual 3D city models have become essential tools for urban planning, traffic management, environmental assessment, and virtual reality applications. Current research largely focuses on constructing high-fidelity city models based on the CityGML standard; however, challenges remain regarding data acquisition costs, complexity of generation processes, and customization capabilities. To address these issues, this study proposes an automated virtual city model generation method that integrates open data (such as OSM, DEM, and open-source LOD2 models) with the concept of digital cousin. This method efficiently generates 3D city models with varying levels of detail, from LOD 0 to LOD 2, by integrating and parameterizing multisource data, including relief, roads, city furniture, vegetation, and buildings. Moreover, it supports flexible user adjustments of key parameters, such as vegetation density, road width, traffic light intervals, building heights, and roof types. Compared with traditional methods that rely on expensive surveying data and labor-intensive manual operations, the proposed approach offers a low-cost, highly flexible, and scalable solution, thereby providing robust support for a wide range of urban simulation and decision-making applications. The code used in this study is as follows:
https://github.com/CBZhao2021/gen3D_virtualCity