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.

翻译：在本文中,我们引入了RaVAEn, 这是一种轻巧、不受监督的方法,用于在卫星数据中进行变化探测,其具体目的是在船上部署;灾害管理等应用极大地受益于卫星观测的迅速提供;传统上,在所有数据都向地面站转移(下链接)之后,数据分析在地面进行;因此,对下行链路能力的限制影响到任何下游应用;相比之下,RaVAEn 预先处理卫星和旗帜上的抽样数据,将区域改为下行链路优先,缩短反应时间;我们核实了由一系列灾难性事件组成的数据集的功效,我们计划与该出版物一起发布该数据集,表明RaVAEn 超越了像素基准。最后,我们测试了评估计算和记忆限制方面资源有限的硬件方法。