  With the advent of deep learning for computer vision tasks, the need for
accurately labeled data in large volumes is vital for any application. The
increasingly available large amounts of solar image data generated by the Solar
Dynamic Observatory (SDO) mission make this domain particularly interesting for
the development and testing of deep learning systems. The currently available
labeled solar data is generated by the SDO mission's Feature Finding Team's
(FFT) specialized detection modules. The major drawback of these modules is
that detection and labeling is performed with a cadence of every 4 to 12 hours,
depending on the module. Since SDO image data products are created every 10
seconds, there is a considerable gap between labeled observations and the
continuous data stream. In order to address this shortcoming, we trained a deep
regression network to track the movement of two solar phenomena: Active Region
and Coronal Hole events. To the best of our knowledge, this is the first
attempt of solar event tracking using a deep learning approach. Since it is
impossible to fully evaluate the performance of the suggested event tracks with
the original data (only partial ground truth is available), we demonstrate with
several metrics the effectiveness of our approach. With the purpose of
generating continuously labeled solar image data, we present this feasibility
analysis showing the great promise of deep regression networks for this task.
