3. Smart Cities and Digital Twins

The landscape of smart cities and urban digital twins has grown significantly over the years. Digital twin platforms have evolved into operational, data-rich, and AI-optimized systems actively used by cities, municipalities, and infrastructure operators. INTERGEO 2025 demonstrated advances in unified data ecosystems, high-precision reality capture, and practical urban applications.

Technologies for capturing the built environment continue to evolve, enabling accurate representations of both indoor and outdoor environments. Indoor point cloud scans are integrated with street-level and aerial point clouds either through control point alignment or cloud-to-cloud registration. SLAM-based systems ensure reliable accuracy in complex indoor environments, while cloud platforms enable semantic enrichment during post-processing.

Immersive Visualization & Game Engines

As user expectations rise for urban digital twins to be both functional and visually immersive, visualization methods continue to evolve. The growing adoption of visualization and streaming standards, along with the introduction of new technologies, has significantly driven this development. Currently, web-based platforms are typically used for visualization, with quality and performance dynamically balanced. Lightweight meshes and standardized streaming formats such as OGC 3D Tiles or OGC I3S are used to efficiently render urban environments. Platforms that prioritize immersive visualization, as exemplified in Figure 4, often rely on established game engines such as Unreal Engine or Unity to achieve realistic representations of city models. Furthermore, the emergence of 3D Gaussian Splatting enables more realistic visualization of urban 3D scenes with greater levels of detail.

Artificial Intelligence as a Driver of Efficiency

The use of AI within urban platforms has increased and enables applications such as natural language data queries and automated reporting. Existing data from already established digital twins can be directly leveraged for this purpose. By lowering technical barriers, AI facilitates interaction with complex geospatial data for non-expert staff and users, thereby contributing to increased efficiency in administrative processes. Platforms are increasingly integrating intelligent advisory and decision-support functions into business workflows. One example is AI-driven fault detection in pipelines and other networks. The results of such inspections are directly used in rehabilitation planning, cost estimation, and strategic asset management.