I.  Overview

Current Urban Digital Twins (UDTs) face several challenges. For instance, scaling Digital Twin systems to manage complex urban environments is difficult. It requires integrating multiple systems and data sources into a cohesive model, which is both complex and resource-intensive. Additionally, data interoperability and system integration remain areas that need further exploration. Managing diverse data sources, achieving real-time data processing, and ensuring system scalability and security are all challenging due to the lack of standardized protocols.

The Urban Digital Twin Interoperability Pilot Project (UDTIP) aimed to create a functional UDT ecosystem by developing and integrating various modules. The project focused on addressing challenges related to spatial information interoperability and promoting the portability and reuse of UDT modules. The pilot was divided into several technical objectives, each with specific deliverables, to demonstrate the value of interoperability to external stakeholders. Key components of the project and their deliverables include:

The UDTIP’s overall architecture is divided into two sections: one supporting noise analysis and the other supporting object detection/classification using Geo-AI. The project used OGC standards to test and evaluate interoperability between modules, with interfaces for both the noise analysis and Geo-AI systems based on OGC APIs. This project’s findings, including the challenges and solutions, contribute to the advancement of urban systems towards the digital twin ideal. The use of OGC standards highlighted their role in enabling interoperability and data sharing across diverse systems.

II.  Executive summary

This initiative addresses the challenges of data interoperability and multi-system integration within Urban Digital Twins (UDTs). It explores the development and interoperability of various modules within an Urban Digital Twin (UDT) framework, leveraging OGC standards to enable seamless data exchange and communication.

The effort focuses on two key applications: noise modeling and Geo-AI analysis.

For noise modeling, the program utilizes 3D city models in CityGML format, traffic profiles, and noise sensor readings. This data is used to simulate and visualize noise levels in urban environments, supporting urban planning and management decisions aimed at mitigating noise pollution.

For Geo-AI analysis, the undertaking processes camera imagery, INS metadata, and labeled training data. This data is converted into formats such as GeoPose and TrainingDML to support machine learning tasks like object detection (e.g., identifying obstacles or illegal dumping) and road surface classification.

The initiative employs various OGC APIs for data access and processing, ensuring interoperability across modules. Key outcomes include the development of prototype APIs, workflows, and visualization tools that demonstrate the value of OGC standards in building a robust and interoperable UDT ecosystem.

This project also emphasizes the importance of stakeholder engagement, involving sponsors such as the Land and Housing Agency of Korea and user groups like the United Nations, to ensure the practical relevance of the UDT applications.

Although the current focus is on two applications, the framework is designed to be extensible, allowing for additional or alternative applications in the future and aims to serve as a reference framework for building UDTs using OGC standards.

III.  Keywords

The following are keywords to be used by search engines and document catalogues.

Urban Digital Twin, Interoperability, OGC Standards, CityGML, GeoPose, TrainDML, OGC APIs, Noise Modeling, Geo-AI, 3D Visualization, Urban Analytics

IV.  Future Outlook

The Urban Digital Twin Interoperability Pilot Project (UDTIP), with its focus on noise modeling and Geo-AI analysis using OGC standards, has a promising future with broad applications across diverse fields. The successful integration of various modules within the UDT framework lays a strong foundation for expansion into areas critical to smart city development and beyond.

UDTs can provide dynamic insights into urban challenges, including—but not limited to—climate change, urban mobility, and critical infrastructure development. For example, integrating ground-based sensors with satellite imagery can enhance our understanding of the urban heat island (UHI) effects caused by urban intensification. Additionally, real-time modeling, simulation, and predictive analytics can be incorporated into UDTs to improve urban traffic flow and overall mobility within cities and regions. The integration of multi-dimensional data (2D, 3D, and real-time) within UDTs also enables utility providers to better manage both underground and above-ground assets, ultimately improving service delivery to communities.

Urban health applications can also benefit significantly from UDT capabilities. Noise pollution—a key focus of the project—is a known contributor to health issues such as stress, sleep disturbances, and cardiovascular problems. By modeling and visualizing noise levels across urban landscapes, city planners can identify hotspots and implement mitigation strategies, ultimately improving public health outcomes. Furthermore, the project’s emphasis on Geo-AI analysis of camera imagery can be extended to monitor environmental factors like air quality, detect potential health hazards such as illegal dumping sites, and assist in crowd management during public health emergencies.

The UDT framework also holds considerable potential for natural disaster preparedness and response. By integrating real-time sensor data from various sources, UDTs can provide dynamic insights into evolving situations during floods, earthquakes, or other disasters. This includes monitoring rising water levels, identifying structurally compromised areas, and tracking the movement of people and emergency response teams. The visualization tools developed through the project can be instrumental in communicating these insights to decision-makers and first responders, enabling more effective and timely interventions.

Moreover, the interoperability aspect of UDTs—facilitated by OGC standards—is particularly relevant for the advancement of autonomous vehicles. Accurate and up-to-date information about the urban environment is essential for safe and efficient navigation. UDTs can provide this information by integrating data from traffic cameras, road sensors, and weather stations. The project’s work on Geo-AI analysis, particularly in road surface classification, can further enhance the perception capabilities of autonomous vehicles, allowing them to adapt to varying road conditions and potential hazards.

With its emphasis on open standards and interoperability, the UDTIP sets the stage for future innovations that can transform how we understand, manage, and interact with our increasingly complex urban environments.

V.  Value Proposition

The value proposition of the UDTIP lies in its ability to integrate diverse urban data and functionalities to create a comprehensive and interoperable digital twin ecosystem. The project focuses on developing mechanisms for inter-module interoperability using OGC standards, which allow for the seamless exchange of data, metadata, and code between different modules, promoting portability and reuse across various urban applications. By applying OGC standards, the project enables the integration of various data, including 3D city models and AI-driven analysis. This integration facilitates a holistic understanding of urban environments and supports the development of a unified platform for accessing and managing diverse geospatial data, eliminating the need to retrieve data from multiple sources. This interoperability is crucial for addressing challenges in areas such as noise pollution and object detection in smart cities, providing a robust platform for urban planning, management, and analysis.