Articles | Volume 15, issue 3
Atmos. Meas. Tech., 15, 797–809, 2022
Atmos. Meas. Tech., 15, 797–809, 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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Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
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Cited articles

Aitken, A., Ledig, C., Theis, L., Caballero, J., Wang, Z., and Shi, W.: Checkerboard artifact free sub-pixel convolution: A note on sub-pixel convolution, resize convolution and convolution resize, arXiv [preprint], arXiv:1707.02937, 2017. a
Blanc, P., Massip, P., Kazantzidis, A., Tzoumanikas, P., Kuhn, P., Wilbert, S., Schüler, D., and Prahl, C.: Short-term forecasting of high resolution local DNI maps with multiple fish-eye cameras in stereoscopic mode, AIP Conference Proceedings, 1850, 140004,, 2017. a
Calbó, J., Long, C. N., González, J.-A., Augustine, J., and McComiskey, A.: The thin border between cloud and aerosol: Sensitivity of several ground based observation techniques, Atmos. Res., 196, 248–260,, 2017. a
Caron, M., Bojanowski, P., Joulin, A., and Douze, M.: Deep clustering for unsupervised learning of visual features, in: Proceedings of the European Conference on Computer Vision (ECCV), pp. 132–149,, 2018. a, b, c, d
Chauvin, R., Nou, J., Thil, S., Traore, A., and Grieu, S.: Cloud detection methodology based on a sky-imaging system, Energy Proced., 69, 1970–1980,, 2015. a
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.