2021/08/31 by Linus Scheibenreif, Scheibenreif, Linus, Michael Mommert +3
Environmental Science · #Air Quality Monitoring and Forecasting #Air Quality and Health Impacts #Atmospheric and Environmental Gas Dynamics #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #I.4 #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2108.13902
openalex publication_date 2021/08/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Air pollution is a major driver of climate change. Anthropogenic emissions from the burning of fossil fuels for transportation and power generation emit large amounts of problematic air pollutants, including Greenhouse Gases (GHGs). Despite the importance of limiting GHG emissions to mitigate climate change, detailed information about the spatial and temporal distribution of GHG and other air pollutants is difficult to obtain. Existing models for surface-level air pollution rely on extensive land-use datasets which are often locally restricted and temporally static. This work proposes a deep learning approach for the prediction of ambient air pollution that only relies on remote sensing data that is globally available and frequently updated. Combining optical satellite imagery with satellite-based atmospheric column density air pollution measurements enables the scaling of air pollution estimates (in this case NO2) to high spatial resolution (up to ∼10m) at arbitrary locations and adds a temporal component to these estimates. The proposed model performs with high accuracy when evaluated against air quality measurements from ground stations (mean absolute error