  The proper classification of plasma regions in near-Earth space is crucial to
perform unambiguous statistical studies of fundamental plasma processes such as
shocks, magnetic reconnection, waves and turbulence, jets and their
combinations. The majority of available studies have been performed by using
human-driven methods, such as visual data selection or the application of
predefined thresholds to different observable plasma quantities. While
human-driven methods have allowed performing many statistical studies, these
methods are often time-consuming and can introduce important biases. On the
other hand, the recent availability of large, high-quality spacecraft
databases, together with major advances in machine-learning algorithms, can now
allow meaningful applications of machine learning to in-situ plasma data. In
this study, we apply the fully convolutional neural network (FCN) deep
machine-leaning algorithm to the recent Magnetospheric Multi Scale (MMS)
mission data in order to classify ten key plasma regions in near-Earth space
for the period 2016-2019. For this purpose, we use available intervals of time
series for each such plasma region, which were labeled by using human-driven
selective downlink applied to MMS burst data. We discuss several quantitative
parameters to assess the accuracy of both methods. Our results indicate that
the FCN method is reliable to accurately classify labeled time series data
since it takes into account the dynamical features of the plasma data in each
region. We also present good accuracy of the FCN method when applied to
unlabeled MMS data. Finally, we show how this method used on MMS data can be
extended to data from the Cluster mission, indicating that such method can be
successfully applied to any in situ spacecraft plasma database.
