The project explored how artificial intelligence (AI), combined with synthetic datasets and rule-based models, can support decision-making in early-stage urban development. Using the generative design platform Hektar as a test bed, the team developed, implemented, and evaluated two complementary computational approaches: a deterministic, explainable algorithm and machine-learning models trained on large-scale synthetic data representing over two million urban plot configurations.The deterministic model demonstrated high precision, achieving less than ±5 % deviation between user-defined targets (FAR, SCR) and resulting outputs for 95 % of test plots. The machine-learning work progressed in two stages. In the first stage, a numerical Multilayer Perceptron (MLP) outperformed a convolutional neural network (CNN) in predicting Site Coverage Ratio (SCR) from compact geometric descriptors. In the second stage, a refined model predicted probability distributions of SCR outcomes, reflecting the stochastic generation of building configurations in the updated Hektar system. Together, these methods established a reproducible workflow that translates user goals into valid spatial outcomes while introducing probabilistic reasoning to early-stage planning.The project demonstrates that small, task-specific AI models can be computationally efficient while delivering substantial benefits. By improving the precision of density and form assessments, such models can contribute to reduced material use, more efficient land allocation, and lower climate impact in the built environment.All predictive models, datasets, and documentation are published openly on GitHub to support further research and industry adoption. By shifting from form generates data to data generates form, the project outlines a scalable pathway toward prescriptive, data-driven urban planning tools capable of supporting more sustainable, evidence-based decisions

The project Sustainable Land Allocation – More Sustainable Construction through Improved Land Allocation Processes was led by RISE Research Institutes of Sweden in collaboration with municipalities, developers, and academic partners. It aimed to support municipalities in setting more consistent, transparent, and effective requirements in land allocation processes, in order to strengthen sustainability outcomes in urban development.

The main result is a practical guidance document that helps municipalities formulate and follow up on requirements and criteria related to economic feasibility, social sustainability, and environmental and climate impact. The guidance was developed iteratively and tested in real-life municipal processes by over 20 municipalities.

The project identified key challenges such as legal uncertainty around technical requirements, the complexity of addressing social values, and the need for adaptable, place-specific criteria. It also fostered collaboration across sectors and laid the foundation for a long-term structure for continued use and development of the guidance.

The guidance is publicly available and intended as a flexible toolbox that municipalities can tailor to their local context and policy goals.

Syftet med förstudien är att skapa ett kunskapsunderlag för hur försöksverksamhet – med särskilt fokus på regulatoriska lösningar – kan användas som verktyg för att främja innovation och klimatomställning i stadsutvecklingen. Genom att kartlägga möjliga former av försöksverksamhet, analysera juridiska och organisatoriska förutsättningar samt identifiera relevanta aktörer och geografiska fokusområden, vill studien stödja Helsingborgs arbete med att utveckla strukturer för test, lärande och samverkan.

Målet är att ge rekommendationer för hur Helsingborg kan arbeta strategiskt med försöksverksamhet, inklusive förslag på konkreta testfall och nästa steg i utvecklingen av en modell för regulatorisk sandlåda eller motsvarande samverkansformer

Pedestrian movement has always been one of the main concerns for urban planning and design, but it has become more important within the sustainable development agenda, as walking is crucial to reducing urban emissions and fostering liveable cities. Therefore, urban planners need to take pedestrian movement into consideration as part of the workflow of planning and designing cities. This study outlines a comprehensive workflow tailored for urban planners. It proposes a hybrid model that integrates an agent-based model, which simulates the micro-scale movement of pedestrians in outdoor urban environments, with a network model, which predicts the aggregated pedestrian flows on a macro-scale. The hybrid model is applied to a pedestrian precinct in the city centre of Gothenburg, Sweden, and is compared to real-world measurements. The reasonable agreement between the simulation results and the real-world data supports the reliability of the proposed workflow, underscoring the model’s ability to statistically predict pedestrian movement on a large scale and individually on a local scale. Furthermore, the model enables the analysis of flow distributions and movement restrictions and facilitates the analysis of different design scenarios and specific pedestrian behaviour. This functionality is valuable for urban design and planning practice, contributing to the optimisation of pedestrian flow dynamics.

Wind comfort is an important factor when new buildings in existing urban areas are planned. It is common practice to use computational fluid dynamics (CFD) simulations to model wind comfort. These simulations are usually time-consuming, making it impossible to explore a high number of different design choices for a new urban development with wind simulations. Data-driven approaches based on simulations have shown great promise, and have recently been used to predict wind comfort in urban areas. These surrogate models could be used in generative design software and would enable the planner to explore a large number of options for a new design. In this paper, we propose a novel machine learning workflow (MLW) for direct wind comfort prediction. The MLW incorporates a regression and a classification U-Net, trained based on CFD simulations. Furthermore, we present an augmentation strategy focusing on generating more training data independent of the underlying wind statistics needed to calculate the wind comfort criterion. We train the models based on different sets of training data and compare the results. All trained models (regression and classification) yield an (Formula presented.) -score greater than 80% and can be combined with any wind rose statistic.