  Humanitarian organizations must have fast and reliable data to respond to
disasters. Deep learning approaches are difficult to implement in real-world
disasters because it might be challenging to collect ground truth data of the
damage situation (training data) soon after the event. The implementation of
recent self-paced positive-unlabeled learning (PU) is demonstrated in this work
by successfully applying to building damage assessment with very limited
labeled data and a large amount of unlabeled data. Self-PU learning is compared
with the supervised baselines and traditional PU learning using different
datasets collected from the 2011 Tohoku earthquake, the 2018 Palu tsunami, and
the 2018 Hurricane Michael. By utilizing only a portion of labeled damaged
samples, we show how models trained with self-PU techniques may achieve
comparable performance as supervised learning.
