Articles | Volume 15, issue 3
Atmos. Meas. Tech., 15, 797–809, 2022
https://doi.org/10.5194/amt-15-797-2022
Atmos. Meas. Tech., 15, 797–809, 2022
https://doi.org/10.5194/amt-15-797-2022

Research article 14 Feb 2022

Research article | 14 Feb 2022

Applying self-supervised learning for semantic cloud segmentation of all-sky images

Yann Fabel et al.

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2021-1', Anonymous Referee #1, 23 Apr 2021
    • AC2: 'Reply on RC1', Yann Fabel, 11 Oct 2021
  • RC2: 'Comment on amt-2021-1', Anonymous Referee #2, 06 Sep 2021
    • AC1: 'Reply on RC2', Yann Fabel, 11 Oct 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Yann Fabel on behalf of the Authors (06 Nov 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish subject to technical corrections (30 Nov 2021) by Szymon Malinowski
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Short summary
This work presents a new approach to exploit unlabeled image data from ground-based sky observations to train neural networks. We show that our model can detect cloud classes within images more accurately than models trained with conventional methods using small, labeled datasets only. Novel machine learning techniques as applied in this work enable training with much larger datasets, leading to improved accuracy in cloud detection and less need for manual image labeling.