  Urban material recognition in remote sensing imagery is a highly relevant,
yet extremely challenging problem due to the difficulty of obtaining human
annotations, especially on low resolution satellite images. To this end, we
propose an unsupervised domain adaptation based approach using adversarial
learning. We aim to harvest information from smaller quantities of high
resolution data (source domain) and utilize the same to super-resolve low
resolution imagery (target domain). This can potentially aid in semantic as
well as material label transfer from a richly annotated source to a target
domain.
