RaVAEn: Unsupervised Change Detection of Extreme Events Using ML On-Board Satellites


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 raVAEn, a lightweight, unsupervised approach for change detection in satellite data based on Variational Auto-Encoders (VAEs), with the specific purpose of on-board deployment. raVAEn 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 raVAEn outperforms pixel-wise baselines. Finally, we tested our approach on resource-limited hardware for assessing computational and memory limitations, simulating deployment on real hardware.

Nature Scientific Reports
Valentina Zantedeschi
Valentina Zantedeschi
Research Scientist

Research Scientist at Human Decision Support located at London, UK.