Digital Twins for city modeling and simulation

The digital twin (DT) (smart city/3D modelling) topic currently attracts the attention and interest of many cities, authorities, industrial and research organizations at national, European and international levels. DT has been seen as a digital replica of the physical living environments that supports decision-making through the seamless integration of a myriad of data and analytics techniques. As such, DT is not a mere geometric (2D and 3D) representation of static assets but a dynamic/live model that represents their past, current, and future states. With that definition in mind, a wide area of research encompassing living environmental modelling, Building Information Modelling (BIM), Geospatial Information Systems (GIS), Internet of Things (IoT), Artificial Intelligence (AI), Virtual Reality (VR), and Urban Decision Support Systems is explored and integrated.

By adopting Big Data and IoT technologies, the city stakeholders are supported in making evidence-based planning and decision-making by implementing data-driven processes using shared data instead of being restricted by access to data. Commonly, data is spread across different organizations without common semantics and a solid technology base, which restricts access to important data The interoperability of data is missing, and it is a target goal of use case scenarios that depend on linking and analysis of heterogeneous data. Therefore, the integration of different data in a common platform is of primary interest. Unfortunately, the current approaches and tools for digital twins/smart cities are not fit to be used at the municipal level, as well as tools are not well equipped to perform continuous monitoring of city processes, collect city data and guide decision-making. In order to support city monitoring and analysis suitable for different domains (land monitoring, urban planning, sustainable development, or economic analysis), to identify problem issues before they arise, to find new opportunities, and to decide through experimentation, a physical city needs to be paired to a virtual one through digital twinning.

This research will focus on applying innovative geospatial methods, and integration of different 3D and IoT data for the creation and application of Digital Twins/3D city models (city or neighbourhood scale).

There are several possible directions:

• A case study in Enschede (The Netherlands). The Municipality of Enschede is interested in digital twinning to address topics such as water storage, development of blue and green infrastructure (urban heat island), traffic monitoring and simulation, air pollution etc. Depending on the data availability and students' interest, the topic will be further elaborated.
• A case study in Johannesburg (South Africa). The Municipality of Johannesburg and the University (South Africa) are interested in digital twinning to address topics such as public safety, traffic/noise monitoring and simulation, etc.  A question to explore can be: How can we make the Sun/Water free again? How do we design a solar / water credit system for poor communities? Depending on the data availability and students' interest, the topic will be further elaborated. See this video:https://village-of-100.gcro.ac.za/ 
• Caribbean case study for hazard management. Small volcanic islands in tropical environments are and could be particularly affected by weather and climate events and as such constitute an important case study to test, validate and implement efficient and comprehensive approaches to tackle multiple and complex hazards. The available multitemporal data (e.g., LiDAR, UAV, SAR, open street map etc.) will used to create a 3D model towards digital twinning of the build-up environment over the terrain. The digital twin will be used for visualisation of the integrated data on the area and provide opportunities for data analysis and scenario simulations. The thematic, temporal imagery and terrain data will be used to assess and simulate the impact of the hazard on the local population and environment (e.g., damage estimation), gain a better understanding of the area with its potential risks and provide suggestions for future mitigation strategies in case of a hazard.