21-22 Sept 2024 |
The aim of the workshop was to identify and develop use cases where digital twins can bridge the gap between planners and citizens, enabling collaboration on climate goals and improving digital public services.
Aims of the government innovation program
Digital twins offer a powerful tool to simulate, monitor and analyse urban environments in real time. They provide planners and decision makers with a dynamic and data-driven understanding of the built environment. These virtual models can be used to optimise the management of infrastructure, predict environmental impacts and test policies before they are implemented.
Beyond these technical applications, digital twins have untapped potential as platforms for citizen engagement. Making these tools more publicly accessible, cities can empower residents to actively shape their urban environment. One key opportunity lies in making digital twins more user-friendly and accessible to both citizens and policymakers.
By simplifying interfaces and improving public access to urban data, cities can democratize urban planning, inviting more transparent and participatory decision-making processes. Empowering residents to interact with and contribute to these models offers a unique avenue for collaboration between planning departments and the communities they serve.
Workshop overview
How differnet GenAI models work?
The workshop started with an introduction to the fundamental concepts of Generative AI. This provided participants with the theoretical foundation they needed to prototype their service design ideas for the digital twin. In the masterclass participants gained insights into how these technologies are built and how they can be applied to urban planning and policy-making.
Outcome
How to use them in un urban design?
The second phase saw a practical application of Generative AI and LLMs in collaborative urban design, building on the theory. Participants applied the concepts learned in the first phase, exploring how these AI models can be used to co-create with citizens, planners, and policymakers. They did so by working in small groups, engaging in interactive exercises to experiment with AI-powered co-design tools.
Outcome
How to reimagine the digital twin with GenAI?
In the final phase, participants synthesised their theoretical knowledge and practical experience to identify concrete new uses and services for the Estonian digital. This will improve public engagement, optimise decision-making and address complex policy challenges. Through collaborative discussions, participants developed potential applications, assessed risks and evaluated the feasibility of their proposed use cases.
Outcome
Developing use cases
Representatives from the Estonian Ministry of Climate, the City of Tallinn, the National Land Use Board, and external stakeholders, including citizens’ committees, planners, and private developers, participated in a collaborative workshop focused on improving urban planning processes through digital twin technology. The workshop addressed issues such as the slow pace of detailed planning, artificial complexity, and barriers to accessing critical information.
Participants worked together to identify positive outcomes, including faster planning discussions, more transparent decision-making, and increased citizen engagement. A key focus was on humanizing the digital twin by co-developing plans with citizens and involving them in idea generation. The group also explored specific topics such as biodiversity, street design, and urban policies, while discussing various methods for deploying the platform, including place-based urban planning, online campaigns, and event-based workshops. The collaboration aimed to develop actionable strategies for making the planning process more inclusive and responsive.
Prototyping
As part of the workshop’s prototyping phase, participants were provided with two digital canvases to develop their use cases. The first canvas followed a structured format, guiding them through key elements such as identifying problems, defining positive impacts, and determining stakeholder involvement, as outlined earlier in the workshop. This helped participants focus on the core challenges and outcomes they aimed to achieve.
The second canvas listed all the generative AI technologies that participants had learned during the first part of the program. This canvas was designed to allow participants to map these technologies onto their use cases, enabling them to prototype and design services around the identified needs. By separating the development of use cases from the design of the services, this approach ensured that participants could first articulate the challenges and goals before integrating advanced AI solutions, fostering a more thoughtful and structured innovation process.
Workshop participants
PROBLEM DEFINITION Current urban planning processes are often slow and difficult for the public to understand or engage with. Artificially imposed deadlines make the translation of strategies inefficient, while citizens frequently feel left out of discussions, resulting in a lack of trust in the planning system.
AMBITION This use case introduces a digital twin platform powered by AI to streamline urban planning discussions and make them more inclusive. By integrating AI tools with citizen feedback loops, the platform will allow for faster, more informed decision-making and foster a sense of participation among stakeholders.
STAKEHOLDERS Primary Stakeholders: Citizens, planners, private developers, architecture studios, and local government officials.
This application allowed users to not only navigate the digital twin of the city but also perform simple GenAI transformations on the existing built environment, such as modifying structures, exploring alternative layouts, and simulating design changes in real-time.
The UrbanistAI team worked closely with workshop attendees, gathering feedback and refining features on the spot, ensuring that the tool was both functional and responsive to the needs of citizens and designers alike.
This working prototype laid the foundation for future iterations of the application, demonstrating that digital twins can be a dynamic, participatory tool for urban development when combined with the capabilities of generative AI.