  A common class of problems in remote sensing is scene classification, a
fundamentally important task for natural hazards identification, geographic
image retrieval, and environment monitoring. Recent developments in this field
rely label-dependent supervised learning techniques which is antithetical to
the 35 petabytes of unlabelled satellite imagery in NASA GIBS. To solve this
problem, we establish CELESTIAL-a self-supervised learning pipeline for
effectively leveraging sparsely-labeled satellite imagery. This pipeline
successfully adapts SimCLR, an algorithm that first learns image
representations on unlabelled data and then fine-tunes this knowledge on the
provided labels. Our results show CELESTIAL requires only a third of the labels
that the supervised method needs to attain the same accuracy on an experimental
dataset. The first unsupervised tier can enable applications such as reverse
image search for NASA Worldview (i.e. searching similar atmospheric phenomenon
over years of unlabelled data with minimal samples) and the second supervised
tier can lower the necessity of expensive data annotation significantly. In the
future, we hope we can generalize the CELESTIAL pipeline to other data types,
algorithms, and applications.
