In-orbit demonstration of a re-trainable machine learning payload for processing optical imagery Article Swipe
Gonzalo Mateo‐García
,
Josh Veitch-Michaelis
,
Cormac Purcell
,
Nicolas Longépé
,
Simon Reid
,
Alice Anlind
,
Fredrik Bruhn
,
James F. Parr
,
Pierre-Philippe Mathieu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-023-34436-w
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-023-34436-w
Cognitive cloud computing in space (3CS) describes a new frontier of space innovation powered by Artificial Intelligence, enabling an explosion of new applications in observing our planet and enabling deep space exploration. In this framework, machine learning (ML) payloads—isolated software capable of extracting high level information from onboard sensors—are key to accomplish this vision. In this work we demonstrate, in a satellite deployed in orbit, a ML payload called ‘WorldFloods’ that is able to send compressed flood maps from sensed images. In particular, we perform a set of experiments to: (1) compare different segmentation models on different processing variables critical for onboard deployment, (2) show that we can produce, onboard, vectorised polygons delineating the detected flood water from a full Sentinel-2 tile, (3) retrain the model with few images of the onboard sensor downlinked to Earth and (4) demonstrate that this new model can be uplinked to the satellite and run on new images acquired by its camera. Overall our work demonstrates that ML-based models deployed in orbit can be updated if new information is available, paving the way for agile integration of onboard and onground processing and “on the fly” continuous learning.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41598-023-34436-w
- https://www.nature.com/articles/s41598-023-34436-w.pdf
- OA Status
- gold
- Cited By
- 29
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4382345294
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4382345294Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1038/s41598-023-34436-wDigital Object Identifier
- Title
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In-orbit demonstration of a re-trainable machine learning payload for processing optical imageryWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-06-27Full publication date if available
- Authors
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Gonzalo Mateo‐García, Josh Veitch-Michaelis, Cormac Purcell, Nicolas Longépé, Simon Reid, Alice Anlind, Fredrik Bruhn, James F. Parr, Pierre-Philippe MathieuList of authors in order
- Landing page
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https://doi.org/10.1038/s41598-023-34436-wPublisher landing page
- PDF URL
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https://www.nature.com/articles/s41598-023-34436-w.pdfDirect 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.nature.com/articles/s41598-023-34436-w.pdfDirect OA link when available
- Concepts
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Payload (computing), Computer science, Orbit (dynamics), Artificial intelligence, Aerospace engineering, Engineering, Computer network, Network packetTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
29Total citation count in OpenAlex
- Citations by year (recent)
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2025: 6, 2024: 19, 2023: 4Per-year citation counts (last 5 years)
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27Number of works referenced by this work
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-
10Other works algorithmically related by OpenAlex
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