We have presented a novel deep learning framework that fuses multiple data sources in order to improve accuracy in air quality forecasts - leading to a wide spectrum of downstream applications that carry significant impact to public health and policy. The work has received a Best Paper Award at the 2022 Workshop on Machine Learning for Earth Observation (MACLEAN’22), held in conjunction with the European Conference on Machine Learning ’22 (ECML/PKDD).
Air pollution is detrimental to human health and its contribution to the global burden of disease is now well known. Accurate prediction of the local surface concentrations of atmospheric pollutants is key to mitigating these harmful effects on human health and the environment.
Motivated by the challenge, our team at CyI has developed a deep learning framework that leverages publicly available data sources including satellite observations, ground station measurements, and elevation and land-use maps in order to improve the modelling accuracy of atmospheric gas and particle pollutants by up to 57%.