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.
翻译:近年来,机器学习算法的增长增加了研究的数量,包括它们在各种不同情景中的可适用性,其中最困难的研究之一是航空航天,这是由于其独特的物理要求;在这方面,在这项工作中介绍了可行性研究和将安装在卫星上的人工智能模型的首个原型;作为案例研究,对火山爆发的探测进行了调查,作为迅速产生警报和允许立即干预的一种方法;提出并设计了两个革命神经网络,表明如何有效地实施这些网络,以查明火山爆发,同时调整其复杂性,以适应机载要求。