On-board Volcanic Eruption Detection through CNNs and Satellite Multispectral Imagery

• URL: http://arxiv.org/abs/2106.15281v1
• Date: Tue, 29 Jun 2021 11:52:43 GMT
• Title: On-board Volcanic Eruption Detection through CNNs and Satellite Multispectral Imagery
• Authors: Maria Pia Del Rosso, Alessandro Sebastianelli, Dario Spiller, Pierre Philippe Mathieu and Silvia Liberata Ullo
• Abstract summary: Authors propose a first prototype and a study of feasibility for an AI model to be 'loaded' on board. As a case study, the authors decided to investigate the detection of volcanic eruptions as a method to swiftly produce alerts. Two Convolutional Neural Networks have been proposed and created, also showing how to correctly implement them on real hardware.
• Score: 59.442493247857755
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, the growth of Machine Learning algorithms in a variety of different applications has raised numerous studies on the applicability of these algorithms in real scenarios. Among all, one of the hardest scenarios, due to its physical requirements, is the aerospace one. In this context, the authors of this work aim to propose a first prototype and a study of feasibility for an AI model to be 'loaded' on board. As a case study, the authors decided to investigate the detection of volcanic eruptions as a method to swiftly produce alerts. Two Convolutional Neural Networks have been proposed and created, also showing how to correctly implement them on real hardware and how the complexity of a CNN can be adapted to fit computational requirements.

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