Unsupervised change detection of extreme events using ML on-board

Conference item

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.

Authors: Ruzicka, V; Vaughan, A; De Martini, D; Fulton, J; Salvatelli, V

• Publication status: Not published
• Peer review status: Peer reviewed
• Publication date: 2021-12-13
• Acceptance date: 2021-10-23
• Event title: NeurIPS Workshop on Artificial Intelligence for Humanitarian Assistance and Disaster Response Workshop (AI+HADR), 2021
• Event location: Virtual Event
• Event website: https://www.hadr.ai/
• Event start date: 2021-12-13
• Event end date: 2021-12-13
• Language: English
• Keywords: FFR