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The capture and transmission of remote-sensed imagery for Earth observation is both computationally and bandwidth expensive. In the analyses of remote-sensed imagery in the visual band, atmospheric cloud cover can obstruct up to two-thirds of observations, resulting in costly imagery being discarded. Mission objectives and satellite operational details vary; however, assuming a cloud-free observation requirement, a doubling of useful data downlinked with an associated halving of delivery cost is possible through effective cloud detection. A minimal-resource, real-time inference neural network is ideally suited to perform automatic cloud detection, both for pre-processing captured images prior to transmission and preventing unnecessary images being taken by larger payload sensors.