Satellite remote sensing and deep learning for aerosols prediction-Reference-Cited by-同舟云学术

Satellite remote sensing and deep learning for aerosols prediction

Published:2023 Issue:1 Volume:71 Page:66-83
ISSN:0042-8469
Container-title:Vojnotehnicki glasnik
language:en
Short-container-title:Vojnotehnički glasnik

Author:

Mirkov Nikola^ORCID,Radivojević Dušan^ORCID,Lazović Ivan^ORCID,Ramadani Uzahir^ORCID,Nikezić Dušan^ORCID

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.

Publisher

Centre for Evaluation in Education and Science (CEON/CEES)

Subject

General Engineering

Link

https://scindeks-clanci.ceon.rs/data/pdf/0042-8469/2023/0042-84692301066M.pdf

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