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Geoscientific Instrumentation, Methods and Data Systems An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/gi-2018-53
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gi-2018-53
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 03 Jun 2019

Research article | 03 Jun 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Instrumentation, Methods and Data Systems (GI).

OzoNet: Atmospheric Ozone Interpolation with Deep Convolutional Neural Networks

Mohamed Akram Zaytar and Chaker El Amrani Mohamed Akram Zaytar and Chaker El Amrani
  • Department of Computer Engineering, Faculty of Sciences and Technology, Tangier, Route Ziaten, P.O. Box 416, Morocco

Abstract. We propose a deep learning method for Atmospheric Ozone Interpolation. Our method directly learns an end-to-end mapping between classically interpolated satellite ozone images and the real ozone measurements. The model's architecture represents a deep stack of convolutions (CNN) that takes the already interpolated images (Using the classical state-of-the-art interpolation method) as Input and outputs a more precise Interpolation of the Region of Interest. Our deep CNN has a lightweight structure, yet demonstrates state-of-the-art interpolation quality, and achieves optimal data processing latency (∆T) for production-ready near-real-time Atmospheric Image Interpolation, which has a big advantage over the state of the art classical interpolation algorithms. We explore different network structures and parameter settings to achieve trade-offs between performance and speed. This method showcases the potential applications of deep learning in Remote Sensing and Climate Science.

Mohamed Akram Zaytar and Chaker El Amrani
Interactive discussion
Status: open (until 30 Jul 2019)
Status: open (until 30 Jul 2019)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Mohamed Akram Zaytar and Chaker El Amrani
Data sets

Bicubic Denoised Patches M. Akram Zaytar and C. El Amrani (Raw Source: EUMETSAT) https://doi.org/10.17605/OSF.IO/3Z9VW

Automatically Mapped Data Points on Morocco M. Akram Zaytar and C. El Amrani (Raw Source: EUMETSAT) https://doi.org/10.17605/OSF.IO/3Z9VW

Artifically Noised Patches M. Akram Zaytar and C. El Amrani (Raw Source: EUMETSAT) https://doi.org/10.17605/OSF.IO/3Z9VW

CSoTA Interpolated Patches M. Akram Zaytar and C. El Amrani (Raw Source: EUMETSAT) https://doi.org/10.17605/OSF.IO/3Z9VW

Model code and software

Remote Sensing Noise Generation with GANs M. Akram Zaytar and C. El Amrani https://doi.org/10.5281/zenodo.3235410

Mohamed Akram Zaytar and Chaker El Amrani
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Latest update: 25 Jun 2019
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