The Φ-Sat-1 mission: the first on-board deep neural network demonstrator for satellite earth observation

Artificial intelligence is paving the way for a new era of algorithms focusing directly on the information contained in the data, autonomously extracting relevant features for a given application. While the initial paradigm was to have these applications run by a server hosted processor, recent advances in microelectronics provide hardware accelerators with an efficient ratio between computation and energy consumption, enabling the implementation of artificial intelligence algorithms 'at the edge'. In this way only the meaningful and useful data are transmitted to the end-user, minimising the required data bandwidth, and reducing the latency with respect to the cloud computing model. In recent years, European Space Agency is promoting the development of disruptive innovative technologies on-board Earth Observation missions. In this field, the most advanced experiment to date is the Φ-sat-1, which has demonstrated the potential of Artificial Intelligence as a reliable and accurate tool for cloud detection on-board a hyperspectral imaging mission. The activities involved included demonstrating the robustness of the Intel Movidius Myriad 2 hardware accelerator against ionising radiation, developing a Cloudscout segmentation neural network, run on Myriad 2, to identify, classify, and eventually discard on-board the cloudy images, and assessing of the innovative Hyperscout-2 hyperspectral sensor. This mission represents the first official attempt to successfully run an AI Deep Convolutional Neural Network (CNN) directly inferencing on a dedicated accelerator on-board a satellite, opening the way for a new era of discovery and commercial applications driven by the deployment of on-board AI