PollutionNet fuses Sentinel-5P satellite data with ground measurements via Vision Transformer self-attention to predict NO2 and SO2 levels with RMSE of 6.89 and 4.49 μg/m³, cutting errors up to 14% versus baselines.
Remote Sensing 13(5):969 Dairi A, Harrou F, Khadraoui S, et al (2021) Integrated multiple directed attention- based deep learning for improved air pollution forecasting
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PollutionNet: A Vision Transformer Framework for Climatological Assessment of NO$_2$ and SO$_2$ Using Satellite-Ground Data Fusion
PollutionNet fuses Sentinel-5P satellite data with ground measurements via Vision Transformer self-attention to predict NO2 and SO2 levels with RMSE of 6.89 and 4.49 μg/m³, cutting errors up to 14% versus baselines.