ISPRS International Journal of Geo-Information

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.

Converting building information models in the Industry Foundation Classes (IFC) into CityGML 3.0 brings building interiors into urban digital twins and knowledge graphs, yet converted models are often assessed only for schema validity. Converters either translate IFC elements one by one (direct translation) or rebuild thematic surfaces, rooms and levels of detail through interpretive decisions (semantic reinterpretation). This study makes those decisions explicit and measurable. We implemented a reinterpretation converter with switchable decisions, an area-preserving polygon reconstruction, and a converter-agnostic GML-to-RDF transformation based on the unmodified LIRIS CityGML 3.0 ontology, GeoSPARQL and PROV-O. On a railway-station model (IFC2x3, 23,861 products), we compared our converter with an open-source direct-translation converter against reference quantities from IFC geometry. For the 3649 objects not simplified in the default run, both converters preserved surface area equally well (95.4% and 95.2% within tolerance); prism simplification multiplied the summed volume of the simplified objects by 7.2. Every IFC product was either converted one-to-one or excluded by a recorded decision, whereas the direct translation’s output lacked 728 products. With curtain walls excluded and simplification applied, the reinterpreted GML was 26 times smaller and answered queries on room area in the standard vocabulary and on wall surfaces by standard class with auxiliary storey and area properties, which the direct translation could not. Controlled runs varying the two decisions independently attributed the volume loss and most of the size reduction to simplification; with it off, reinterpretation matched direct translation on all 23,133 common objects (98.7% versus 98.6% within tolerance).

Urban functional area identification is fundamental to understanding urban spatial structure. Existing approaches typically assign a single category to each basic spatial unit, but are unable to fully represent variations in functional intensity. In this study, we introduced a refined urban functional area identification method that explicitly accounts for the heterogeneous spatial influence of points of interest (POIs) using Changzhou as a case study. The functional intensity of individual POIs was evaluated by integrating the entity scale, public recognition, and facility hierarchy, and an adaptive influence radius was constructed based on the functional intensity of individual POIs and their spatial clustering characteristics. A Gaussian distance-decay model was introduced to characterize the attenuation of the POI functional influence with increasing distance. The dominant functional areas, mixed-use functional areas, and functional transition areas were identified through functional structure analysis, function determination, and spatial aggregation. Comparison with land-use parcels showed that the method effectively identifies dominant functions. The identified functional categories, functional composition, and spatial variations in functional intensity within representative areas were highly consistent with the actual distribution of urban features. This study provides a new methodological framework for differentiated urban functional analysis and refined identification of urban functional areas.