  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.
