  Synthetic Aperture Radar (SAR) data and Interferometric SAR (InSAR) products
in particular, are one of the largest sources of Earth Observation data. InSAR
provides unique information on diverse geophysical processes and geology, and
on the geotechnical properties of man-made structures. However, there are only
a limited number of applications that exploit the abundance of InSAR data and
deep learning methods to extract such knowledge. The main barrier has been the
lack of a large curated and annotated InSAR dataset, which would be costly to
create and would require an interdisciplinary team of experts experienced on
InSAR data interpretation. In this work, we put the effort to create and make
available the first of its kind, manually annotated dataset that consists of
19,919 individual Sentinel-1 interferograms acquired over 44 different
volcanoes globally, which are split into 216,106 InSAR patches. The annotated
dataset is designed to address different computer vision problems, including
volcano state classification, semantic segmentation of ground deformation,
detection and classification of atmospheric signals in InSAR imagery,
interferogram captioning, text to InSAR generation, and InSAR image quality
assessment.
