A Standards-Aligned Framework for Real-Time Urban Geovisualization: Generalized Data Models and Rule-Based Representation and Encoding Selection

Real-time sensing platforms such as closed-circuit television (CCTV) and unmanned aerial vehicles (UAVs) continuously produce large volumes of urban observation data, yet the path from such streams to map-based visualization is usually built case by case. This study proposes a standards-aligned framework for real-time urban geovisualization that connects heterogeneous sensing data to 2D and 3D map representations. The framework comprises (i) a generalized dynamic urban information model that structures pedestrians, personal-mobility devices, vehicles, and fixed objects through a common core schema with object-specific extensions; (ii) a multidimensional classification of dynamic information by spatial support, temporal behavior, representation dimension, and encoding class; and (iii) transparent, rule-based procedures that select the representation dimension (2D/3D) and the encoding profile—vector data for 2D networks and areas, batched and instanced 3D content for quasi-static backgrounds and repeated state-updating objects, and a time-dynamic format for trajectories—under the constraints of the implemented web-based prototype. The framework was instantiated across five urban-monitoring scenario categories comprising six visualization cases: moving-object trajectories, side-road and arterial-road congestion, pedestrian environment, street-tree vitality, and event-based crowd density. The analysis is a scenario-based instantiation and representation-coverage analysis; controlled performance benchmarks and user experiments were not conducted and are identified as future work. The results indicate that a unified data-model framework and selection procedure can serve heterogeneous urban sensing applications, providing a documented, reusable basis for real-time urban geovisualization services.