Sophisticated spatial and spatio-temporal exposure models are urgently needed to better reflect real-life exposures and to comprehensively determine and understand the long-term impact of environmental factors on health. Furthermore, advanced statistical and data science approaches are needed to elucidate and understand the complex interplay between the environment and population health. Currently available models are hampered by the trade-off between complexity and interpretability as well as the biased nature of population-based cohort data. This project aims on solving these challenges by developing data science methods in the domains of Artificial Intelligence (AI) and Machine Learning (ML) to advance currently available noise maps, improve the quantification of noise impacts on health and delineate the complex interplay between environmental, contextual and individual socio-economic and health data. Methodically, three objectives are
Illustration of satellite data converging to GIS information, noise maps and an exposure assessment for individual households eventually.