On Board Volcanic Eruption Detection through CNNs and Satellite Multispectral Imagery
This work addresses the need for swift volcanic eruption alerts to enable immediate interventions, but it is incremental as it applies existing CNNs to a new aerospace domain with specific adaptations.
The paper tackles the problem of detecting volcanic eruptions using satellite imagery by proposing two Convolutional Neural Networks (CNNs) designed for on-board satellite deployment, achieving feasibility and a prototype that efficiently identifies eruptions while meeting hardware constraints.
In recent years, the growth of Machine Learning (ML) algorithms has raised the number of studies including their applicability in a variety of different scenarios. Among all, one of the hardest ones is the aerospace, due to its peculiar physical requirements. In this context, a feasibility study and a first prototype for an Artificial Intelligence (AI) model to be deployed on board satellites are presented in this work. As a case study, the detection of volcanic eruptions has been investigated as a method to swiftly produce alerts and allow immediate interventions. Two Convolutional Neural Networks (CNNs) have been proposed and designed, showing how to efficiently implement them for identifying the eruptions and at the same time adapting their complexity in order to fit on board requirements.