Future Urban Development: AI as a Catalyst for Sustainable Decisions

Urban development is a complex and time-consuming process that requires a vast amount of information to achieve the right balance between economic, social, and environmental objectives. Data and lessons learned from past projects often remain unused, leading to inefficient processes and lost knowledge.

The project utilises AI and generative, rule-based models to make managing property development more straightforward and precise. New methods to automate and streamline the process enable users to generate well-founded decision-making frameworks quickly. We can analyse extensive datasets from previous projects through the Hektar platform, a digital solution developed by the project partner Parametric Solutions. With Hektar, hundreds of design proposals can be generated and evaluated within a short timeframe—a previously laborious task. Using training data from Hektar, the project can incorporate sustainability and social and economic aspects into the development of the AI model, thereby ensuring that the generated outputs are both sustainable and well-grounded.

The project uses machine learning to quickly and accurately evaluate generated design proposals and identify those that best meet the specified requirements. In doing so, the project aims to facilitate more sustainable decisions that benefit society and the environment.

We are working systematically on design sprints involving data collection, development, implementation, and testing during spring 2025. Once we have tested the methods on real-world projects through an appointed reference group of users, we will focus on disseminating knowledge and experiences during autumn through conferences, seminars, and collaborations with industry stakeholders.

Project Goals

The project aims to reduce both time and costs for property development while enhancing the sustainability of realised developments. AI-based tools will enable municipalities, property developers, and consultants to quickly create scenarios and make decisions based on reliable data, ultimately leading to better-planned and more environmentally friendly cities.