  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. Applications such as disaster
management enormously benefit from the rapid availability of satellite
observations. Traditionally, data analysis is performed on the ground after all
data is transferred - downlinked - to a ground station. Constraint on the
downlink capabilities therefore affects any downstream application. In
contrast, 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 composed of time series of
catastrophic events - which we plan to release alongside this publication -
demonstrating that RaVAEn outperforms pixel-wise baselines. Finally we tested
our approach on resource-limited hardware for assessing computational and
memory limitations.
