Evaluating Self and Semi-Supervised Methods for Remote Sensing Segmentation Tasks

We perform a rigorous evaluation of recent self and semi-supervised ML techniques that leverage unlabeled data for improving downstream task performance, on three remote sensing tasks of riverbed segmentation, land cover mapping and flood mapping. These methods are especially valuable for remote sensing tasks since there is easy access to unlabeled imagery and getting ground truth labels can often be expensive. We quantify performance improvements one can expect on these remote sensing segmentation tasks when unlabeled imagery (outside of the labeled dataset) is made available for training. We also design experiments to test the effectiveness of these techniques when the test set has a domain shift relative to the training and validation sets.