  This paper describes Georeference Contrastive Learning of visual
Representation (GeoCLR) for efficient training of deep-learning Convolutional
Neural Networks (CNNs). The method leverages georeference information by
generating a similar image pair using images taken of nearby locations, and
contrasting these with an image pair that is far apart. The underlying
assumption is that images gathered within a close distance are more likely to
have similar visual appearance, where this can be reasonably satisfied in
seafloor robotic imaging applications where image footprints are limited to
edge lengths of a few metres and are taken so that they overlap along a
vehicle's trajectory, whereas seafloor substrates and habitats have patch sizes
that are far larger. A key advantage of this method is that it is
self-supervised and does not require any human input for CNN training. The
method is computationally efficient, where results can be generated between
dives during multi-day AUV missions using computational resources that would be
accessible during most oceanic field trials. We apply GeoCLR to habitat
classification on a dataset that consists of ~86k images gathered using an
Autonomous Underwater Vehicle (AUV). We demonstrate how the latent
representations generated by GeoCLR can be used to efficiently guide human
annotation efforts, where the semi-supervised framework improves classification
accuracy by an average of 10.2% compared to the state-of-the-art SimCLR using
the same CNN and equivalent number of human annotations for training.
