KappaMask: AI-Based Cloudmask Processor for Sentinel-2 Article Swipe
Marharyta Domnich
,
Indrek Sünter
,
Heido Trofimov
,
Olga Wold
,
Fariha Harun
,
Anton Kostiukhin
,
Mihkel Järveoja
,
Mihkel Veske
,
Tanel Tamm
,
Kaupo Voormansik
,
Aire Olesk
,
Valentina Boccia
,
Nicolas Longépé
,
Enrico Cadau
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/rs13204100
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/rs13204100
The Copernicus Sentinel-2 mission operated by the European Space Agency (ESA) provides comprehensive and continuous multi-spectral observations of all the Earth’s land surface since mid-2015. Clouds and cloud shadows significantly decrease the usability of optical satellite data, especially in agricultural applications; therefore, an accurate and reliable cloud mask is mandatory for effective EO optical data exploitation. During the last few years, image segmentation techniques have developed rapidly with the exploitation of neural network capabilities. With this perspective, the KappaMask processor using U-Net architecture was developed with the ability to generate a classification mask over northern latitudes into the following classes: clear, cloud shadow, semi-transparent cloud (thin clouds), cloud and invalid. For training, a Sentinel-2 dataset covering the Northern European terrestrial area was labelled. KappaMask provides a 10 m classification mask for Sentinel-2 Level-2A (L2A) and Level-1C (L1C) products. The total dice coefficient on the test dataset, which was not seen by the model at any stage, was 80% for KappaMask L2A and 76% for KappaMask L1C for clear, cloud shadow, semi-transparent and cloud classes. A comparison with rule-based cloud mask methods was then performed on the same test dataset, where Sen2Cor reached 59% dice coefficient for clear, cloud shadow, semi-transparent and cloud classes, Fmask reached 61% for clear, cloud shadow and cloud classes and Maja reached 51% for clear and cloud classes. The closest machine learning open-source cloud classification mask, S2cloudless, had a 63% dice coefficient providing only cloud and clear classes, while KappaMask L2A, with a more complex classification schema, outperformed S2cloudless by 17%.
Related Topics
Concepts
Cloud computing
Computer science
Remote sensing
Cloud top
Sørensen–Dice coefficient
Artificial intelligence
Segmentation
Image segmentation
Geology
Operating system
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs13204100
- https://www.mdpi.com/2072-4292/13/20/4100/pdf?version=1634200896
- OA Status
- gold
- Cited By
- 37
- References
- 24
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3207866056
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3207866056Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/rs13204100Digital Object Identifier
- Title
-
KappaMask: AI-Based Cloudmask Processor for Sentinel-2Work title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-10-13Full publication date if available
- Authors
-
Marharyta Domnich, Indrek Sünter, Heido Trofimov, Olga Wold, Fariha Harun, Anton Kostiukhin, Mihkel Järveoja, Mihkel Veske, Tanel Tamm, Kaupo Voormansik, Aire Olesk, Valentina Boccia, Nicolas Longépé, Enrico CadauList of authors in order
- Landing page
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https://doi.org/10.3390/rs13204100Publisher landing page
- PDF URL
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https://www.mdpi.com/2072-4292/13/20/4100/pdf?version=1634200896Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2072-4292/13/20/4100/pdf?version=1634200896Direct OA link when available
- Concepts
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Cloud computing, Computer science, Remote sensing, Cloud top, Sørensen–Dice coefficient, Artificial intelligence, Segmentation, Image segmentation, Geology, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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37Total citation count in OpenAlex
- Citations by year (recent)
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2025: 8, 2024: 15, 2023: 8, 2022: 6Per-year citation counts (last 5 years)
- References (count)
-
24Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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