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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Cited articles

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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.