Urban Digital Twins (UDTs) represent a powerful tool for understanding and managing the complex dynamics of urban environments. However, current UDT implementations often face challenges related to data interoperability, system integration, and scalability. Urban digital twins must integrate data from various sources, such as sensors, 3D models, and IoT devices, which often have different formats and semantics. This hetero-geneity complicates data interpretation and integration (Gu et al., 2024, Rafamatanantsoa et al., 2024). Moreover, The com-plexity of urban environments, with their diverse stakeholders and systems, makes it challenging to achieve interoperability. Stakeholders often struggle to understand and manage the com-plexity involved in integrating various data sources (Raes et al., 2021) Addressing these gaps, the Urban Digital Twin Interoperabil-ity Pilot (UDTIP)1, organized by the Open Geospatial Consor-tium (OGC), explores the development of a functional and in-teroperable UDT ecosystem through the integration of diverse urban data and standards-based workflows. The project fo-cuses on two main applications: urban traffic noise modeling and Geo-AI analysis. Noise modeling leverages 3D city mod-els in CityGML format, traffic profiles, and sensor readings to simulate and visualize urban noise levels, providing insights for planning and mitigation strategies. For Geo-AI analysis, camera imagery, INS metadata, and labeled training data are processed to enable object detection and road surface classi-fication tasks within urban environments. Central to the pro-ject is the use of OGC APIs to ensure seamless data exchange between modules. By aligning persistent elements, such as 3D city models, with dynamic information from IoT sensors and AI-driven analysis, the project demonstrates a viable pathway towards scalable and modular urban digital twins. Furthermore, stakeholder engagement with organizations such as the Land and Housing Agency of Korea and the United Nations ensures that the project outcomes address real-world needs and priorities.