Title of article
Satellite remote sensing and deep learning for aerosols prediction
Author/Authors
Mirkov ، Nikola S. “Vinča” Institute of Nuclear Sciences - National Institute of the Republic of Serbia - University of Belgrade , Radivojević ، Dušan S. “Vinča” Institute of Nuclear Sciences - National Institute of the Republic of Serbia - University of Belgrade , Lazović ، Ivan M. “Vinča” Institute of Nuclear Sciences - National Institute of the Republic of Serbia - University of Belgrade , Ramadani ، Uzahir R. “Vinča” Institute of Nuclear Sciences - National Institute of the Republic of Serbia - University of Belgrade , Nikezić ، Dušan P. “Vinča” Institute of Nuclear Sciences - National Institute of the Republic of Serbia - University of Belgrade
From page
66
To page
83
Abstract
Introduction/purpose: The paper presents a new state-of-the-art method that involves NASA satellite imagery with the latest deep learning model for a spatiotemporal sequence forecasting problem. Satellite-retrieved aerosol information is very useful in many fields such as PM prediction or COVID-19 transmission. The input data set was MODAL2_E_AER_OD which presents global AOT for every 8 days from Terra/MODIS. The implemented machine learning algorithm was built with ConvLSTM2D layers in Keras. The obtained results were compared with the new CNN LSTM model. Methods: Computational methods of Machine Learning, Artificial Neural Networks, Deep Learning. Results: The results show global AOT prediction obtained using satellite digital imagery as an input. Conclusion: The results show that the ConvLSTM developed model could be used for global AOT prediction, as well as for PM and COVID-19 transmission.
Keywords
aerosol optical thickness , NASA Earth observations , ConvLSTM2D , Covid , 19 , particulate matter dispersion
Journal title
Military Technical Courier
Journal title
Military Technical Courier
Record number
2736276
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