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Geoscientific Instrumentation, Methods and Data Systems An interactive open-access journal of the European Geosciences Union
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Preprints
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.

  03 Jun 2019

03 Jun 2019

Review status
This preprint has been withdrawn by the authors.

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.

This preprint has been withdrawn.

Mohamed Akram Zaytar and Chaker El Amrani

Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

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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