  In the field of post-disaster assessment, for timely and accurate rescue and
localization after a disaster, people need to know the location of damaged
buildings. In deep learning, some scholars have proposed methods to make
automatic and highly accurate building damage assessments by remote sensing
images, which are proved to be more efficient than assessment by domain
experts. However, due to the lack of a large amount of labeled data, these
kinds of tasks can suffer from being able to do an accurate assessment, as the
efficiency of deep learning models relies highly on labeled data. Although
existing semi-supervised and unsupervised studies have made breakthroughs in
this area, none of them has completely solved this problem. Therefore, we
propose adopting a self-supervised comparative learning approach to address the
task without the requirement of labeled data. We constructed a novel asymmetric
twin network architecture and tested its performance on the xBD dataset.
Experiment results of our model show the improvement compared to baseline and
commonly used methods. We also demonstrated the potential of self-supervised
methods for building damage recognition awareness.
