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.