Integrating Advanced AI techniques to assist Urban Digital Twins Generation

Abstract. Digital twins play a crucial role in autonomous driving applications and transportation system simulations. The need for large scale and dynamic information has increased interest in generating urban digital twins from remote sensing data. Aerial high resolution imagery of urban areas serves as the one of the most important data sources for this task. Advances in deep learning and machine learning allow more accurate and automated extraction of urban elements. In recent years, we have developed and integrated advanced deep learning models to extract various land cover types surrounding road networks, including buildings, roads, and vegetation. Furthermore, we have conducted proof of concept studies aimed at detecting and delineating linear landmarks from aerial imagery, including curbstones and road borders. These developments contribute to the creation of more accurate and detailed urban digital twins, which are essential for advanced urban analytics and intelligent transportation systems.
Results from the deep learning models are presented for the Schwarzer Berg district in Brunswick, Germany, which is a test region for the development of mobility services and technologies at the German Aerospace Center (DLR). The AI models are trained using benchmark datasets from other urban regions, indicating that the proposed approaches can be readily transferred and evaluated in other European cities.