Urban digital twins should reflect the urban infrastructure semantically and geometrically as accurately as possible, but also in a timely manner. Therefore, regular updating of existing existing point clouds is necessary in order to do justice to infrastructural changes, e.g. in the context of construction measures. For this purpose, cities often use terrestrial laser scanning or mobile ground-based laser scanning (car, bicycle, backpack), as this allows small areas to be re-measured efficiently. These "new" point clouds must then be efficiently integrated into the existing point clouds so that consistent geoinformation is created. This presentation discusses solutions in this regard and describes a workflow that automatically registers point clouds in as-built point clouds despite many outliers and disturbance points, depending on the geometric richness of the urban development.

The use of mobile mapping vehicles to capture the environment of infrastructure networks, such as roads, is well established today. Infrastructure operators, including cities and state agencies, either operate their own mapping systems or contract service providers to regularly capture 3D point clouds, image data, and even subsurface radar data. However, this data is often used only for isolated, site-specific viewing or manual analyses. Cross-application utilization and a comprehensive view of entire infrastructure networks are frequently missing, leaving much of the data’s potential untapped. To address this, the goal should be to leverage the collected data across a range of applications, such as planning, monitoring, and strategic decision support, by turning it into a digital twin of the infrastructure. We demonstrate how modern sensing technologies, AI-based analytics, and scalable system architectures can be used to generate detailed digital twins that support both maintenance activities and predictive infrastructure planning. We present several use cases from road, rail, and powerline networks to illustrate the versatility and applicability of this approach.