  Machine learning, particularly deep learning, is being increasing utilised in
space applications, mirroring the groundbreaking success in many earthbound
problems. Deploying a space device, e.g. a satellite, is becoming more
accessible to small actors due to the development of modular satellites and
commercial space launches, which fuels further growth of this area. Deep
learning's ability to deliver sophisticated computational intelligence makes it
an attractive option to facilitate various tasks on space devices and reduce
operational costs. In this work, we identify deep learning in space as one of
development directions for mobile and embedded machine learning. We collate
various applications of machine learning to space data, such as satellite
imaging, and describe how on-device deep learning can meaningfully improve the
operation of a spacecraft, such as by reducing communication costs or
facilitating navigation. We detail and contextualise compute platform of
satellites and draw parallels with embedded systems and current research in
deep learning for resource-constrained environments.
