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In this paper, we introduce RaVAEn, a lightweight, unsupervised approach forchange detection in satellite data based on Variational Auto-Encoders (VAEs)with the specific purpose of on-board deployment. Applications such as disastermanagement enormously benefit from the rapid availability of satelliteobservations. Traditionally, data analysis is performed on the ground after alldata is transferred - downlinked - to a ground station. Constraint on thedownlink capabilities therefore affects any downstream application. Incontrast, RaVAEn pre-processes the sampled data directly on the satellite andflags changed areas to prioritise for downlink, shortening the response time.We verified the efficacy of our system on a dataset composed of time series ofcatastrophic events - which we plan to release alongside this publication -demonstrating that RaVAEn outperforms pixel-wise baselines. Finally we testedour approach on resource-limited hardware for assessing computational andmemory limitations.

Unsupervised Change Detection of Extreme Events Using ML On-Board