Journal article
RaVÆn: unsupervised change detection of extreme events using ML on-board satellites
- Abstract:
- Applications such as disaster management enormously benefit from rapid availability of satellite observations. Traditionally, data analysis is performed on the ground after being transferred—downlinked—to a ground station. Constraints on the downlink capabilities, both in terms of data volume and timing, therefore heavily affect the response delay of any downstream application. In this paper, we introduce RaVÆn, a lightweight, unsupervised approach for change detection in satellite data based on Variational Auto-Encoders (VAEs), with the specific purpose of on-board deployment. RaVÆn pre-processes the sampled data directly on the satellite and flags changed areas to prioritise for downlink, shortening the response time. We verified the efficacy of our system on a dataset—which we release alongside this publication—composed of time series containing a catastrophic event, demonstrating that RaVÆn outperforms pixel-wise baselines. Finally, we tested our approach on resource-limited hardware for assessing computational and memory limitations, simulating deployment on real hardware.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Version of record, pdf, 1.8MB, Terms of use)
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- Publisher copy:
- 10.1038/s41598-022-19437-5
Authors
- Publisher:
- Springer Nature
- Journal:
- Scientific reports More from this journal
- Volume:
- 12
- Article number:
- 16939
- Publication date:
- 2022-10-08
- Acceptance date:
- 2022-08-29
- DOI:
- EISSN:
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2045-2322
- Pmid:
-
36209278
- Language:
-
English
- Keywords:
- Pubs id:
-
1282100
- Local pid:
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pubs:1282100
- Deposit date:
-
2022-11-07
Terms of use
- Copyright holder:
- Růžička et al.
- Copyright date:
- 2022
- Rights statement:
- Copyright © 2022, The Author(s) This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
- Licence:
- CC Attribution (CC BY)
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