Artificial Intelligence (AI) has the potential to revolutionize space exploration by delegating spacecraft decisions to onboard AI. The onboard neural-network (NN) will have parameters that can be updated onboard by telecommands after training on ground. However, Satellite uplinks have limited bandwidth and transmissions can be costly. Furthermore, a suboptimal NN will miss valuable scientific data. Smaller networks can therefore decrease the uplink cost and increase the value of the downlinked data. In this work, we evaluate and discuss using reduced-precision and small NNs to reduce the upload time. As an example of a mission where AI could be used, we focus on NASA's Magnetospheric MultiScale (MMS) mission. We showcase how an onboard AI can be used in the Earth's magnetosphere to classify data for selective downlink or recognize a region of interest to trigger a burst-mode, collecting data at a high-rate. Using a simple algorithm, we show the detection of a region of interest in on a stream of classifications. To provide the classifications, we use a Convolutional Neural Network (CNN) trained to an accuracy >94%. We show how the NN can be reduced to a single linear layer without accuracy loss. Thereby, Reducing the upload time by up to 98.9%. Each network can be reduced further by using lower-precision formats, changing the accuracy by less than 0.6 percentage points.