ArticleScientific reports2022
RaVÆn: unsupervised change detection of extreme events using ML on-board satellites.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- STTORM-CD low-demand and high-impact disaster monitoring onboard satellites using change detection.Scientific reports · 2026Article
- Optimizing deep learning models for on-orbit deployment through neural architecture search.Scientific reports · 2025Article
- Training-free AI for earth observation change detection using physics aware neuromorphic networks.Scientific reports · 2025Article
- Semantic segmentation of methane plumes with hyperspectral machine learning models.Scientific reports · 2023Article
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Authors and funding
8 authors.
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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.
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