Unsupervised Change Detection of Extreme Events Using ML On-Board

• URL: http://arxiv.org/abs/2111.02995v1
• Date: Thu, 4 Nov 2021 16:45:15 GMT
• Title: Unsupervised Change Detection of Extreme Events Using ML On-Board
• Authors: V\'it R\r{u}\v{z}i\v{c}ka, Anna Vaughan, Daniele De Martini, James Fulton, Valentina Salvatelli, Chris Bridges, Gonzalo Mateo-Garcia, Valentina Zantedeschi
• Abstract summary: We introduce RaVAEn, a lightweight, unsupervised approach for change detection in satellite data based on Variational Auto-Encoders (VAEs) RaVAEn pre-processes the sampled data directly on the satellite and flags changed areas to shorten downlink, the response time. We verified the efficacy of our system on a dataset composed of time series of catastrophic events.
• Score: 3.1955314117075715
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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