Through applying cutting-edge approaches and utilizing high-resolution population, air quality and urban structural data, and state-of-the-art research methods from various disciplines, the CousCOUS project aims to answer the following:
What are the main factors in the urban structure causing formation of air quality hotspots?
Is there a relationship between socioeconomic status and air quality?
How can we predict future population using AI-modelling utilizing high-resolution data on population and GIS data about built environment structures?
How should the reward function in Reinforcement Learning be composed to reliably accommodate information of very different nature (traffic, population, air quality) to provide meaningful suggestions for city planning?
How well Machine Learning models built, trained and tested in one city can be scaled for other cities in Europe?
By working together and in close co-operation with city planners, we ensure that our research will have real life impact, especially in light of planning a new city boulevard in Helsinki.
Explore further our work thematically! Each of the following pages correspond to work packages of the project.