Oxford University researchers train a machine learning model in outer space

A group of researchers from Oxford University have trained a machine learning model in outer space, on board a satellite.

The success of the training means that progress has been made towards enabling real-time monitoring and decision making for a range of applications, from disaster management to deforestation.

Researchers trained a simple model to detect changes in cloud cover from aerial images directly onboard the satellite, in contrast to training on the ground.

DPhil Student and team leader, Vít Růžička, said: ''The model we developed, called RaVAEn, first compresses the large image files into vectors of 128 numbers. During the training phase, the model learns to keep only the informative values in this vector; the ones that relate to the change it is trying to detect – in this case, whether there is a cloud present or not. This results in extremely fast training due to having only a very small classification model to train.''

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Normally, developing a machine learning model would require several rounds of training, using the power of a cluster of linked computers. In contrast, the team’s tiny model completed the training phase, using over 1300 images, in around one and a half seconds.

Performing machine learning in outer space could also help overcome the problem of on-board satellite sensors being affected by the harsh environmental conditions which leave said sensors in frequent need of re-calibration.

Vít Růžička added: ''Our proposed system could be used in constellations of non-homogeneous satellites, where reliable information from one satellite can be applied to train the rest of the constellation. This could be used, for instance, to recalibrate sensors that have degraded over time or experienced rapid changes in the environment.''

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