  Self- and semi-supervised machine learning techniques leverage unlabeled data
for improving downstream task performance. These methods are especially
valuable for remote sensing tasks where producing labeled ground truth datasets
can be prohibitively expensive but there is easy access to a wealth of
unlabeled imagery. We perform a rigorous evaluation of SimCLR, a
self-supervised method, and FixMatch, a semi-supervised method, on three remote
sensing tasks: riverbed segmentation, land cover mapping, and flood mapping. We
quantify performance improvements on these remote sensing segmentation tasks
when additional imagery outside of the original supervised dataset is made
available for training. We also design experiments to test the effectiveness of
these techniques when the test set is domain shifted to sample different
geographic areas compared to the training and validation sets. We find that
such techniques significantly improve generalization performance when labeled
data is limited and there are geographic domain shifts between the training
data and the validation/test data.
