Abstract. Urban Digital Twins are powerful tools designed to replicate and analyze the dynamics of urban environments, supporting more informed planning, management, and decision-making. However, their development is often challenged by issues in data interoperability, system integration, and scalability. This paper explores the pivotal role and technical implementation of new-generation Open Geospatial Consortium (OGC) APIs—Features, 3D GeoVolumes, Tiles, and SensorThings—in fostering seamless, lightweight, and scalable data exchange to overcome these barriers. These modern, RESTful APIs surpass older standards like WFS and WMS by simplifying integration and enhancing compatibility with diverse data sources, such as 3D city models in CityGML, IoT sensor data, and geo-referenced imagery. Through the OGC Urban Digital Twin Interoperability Pilot (UDTIP), the paper illustrates the practical application of these APIs in two use cases: urban traffic noise modeling and Geo-AI analysis. By integrating 3D city models, traffic profiles, sensor data, and imagery, UDTIP enables noise simulation and advanced tasks like object detection and road surface classification. Its modular architecture supports efficient data exchange across vector, raster, sensor, and training datasets, leading to impactful geovisualizations powered by CesiumJS, which renders noise patterns and urban features as 3D Tiles and point clouds. By harnessing OGC standards in the UDTIP, our OGC API powered data integration and visualization framework establishes a robust, interoperable framework for scalable UDTs, delivering actionable insights for urban planning and management while promoting standardized, future-ready digital twin solutions.
Santhanavanich et al. (Thu,) studied this question.
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