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Digital twin is an emerging technology data from a variety of sensors to create a virtual replica of physical objects, processes, and systems. It is becoming increasingly popular as cities plan to become smarter and more efficient. Despite the growing amount of data, the main focus of urban digital twin applications has been dedicated to 3D building models that come often in the international OGC standard CityGML. For example, the German federal Agency for Cartography and Geodesy has published nation-wide 3D building models. While so far models of buildings have been a focus, 3D representations of roads according to CityGML are increasingly available due to emerging applications and data gathering methods. Thus, it is important to develop the required interfaces to make SUMO compatible with the rest of the components in an urban digital twin. In a recent research project, city of Munich conducted a data collection campaign where data from aerial images and multiple on-ground sensors were fused to extract geographical data including road geometries and lane markings. A convertor has been developed to extract the necessary attributes from the point cloud to generate SUMO-PlainXML files. Using the NETCONVERT, a SUMO network and its corresponding OpenDrive were generated, which was then used by another convertor to create a network based on the CityGML 3.0 standard. The later format allows for integrating the network objects as well as the results of the simulation of the simulation in a wider set of geographical applications such as Cesium for not only 3D visualisations, but also storing the data in 3DCityDatabase (3DCityDB) or even coupling the simulation with game engines. This is particularly important for certain applications such as modelling emission dispersion that requires buildings and vegetation or analysis of autonomous vehicles in real urban environment for an accurate modelling of multiple sensors. The entire process and the set of tools were applied on a small intersection in Munich.