  Globally 800 million people live within 100 km of a volcano and currently
1500 volcanoes are considered active, but half of these have no ground-based
monitoring. Alternatively, satellite radar (InSAR) can be employed to observe
volcanic ground deformation, which has shown a significant statistical link to
eruptions. Modern satellites provide large coverage with high resolution
signals, leading to huge amounts of data. The explosion in data has brought
major challenges associated with timely dissemination of information and
distinguishing volcano deformation patterns from noise, which currently relies
on manual inspection. Moreover, volcano observatories still lack expertise to
exploit satellite datasets, particularly in developing countries. This paper
presents a novel approach to detect volcanic ground deformation automatically
from wrapped-phase InSAR images. Convolutional neural networks (CNN) are
employed to detect unusual patterns within the radar data.
