  Although convolutional neural networks have been proven to be an effective
tool to generate high quality maps from remote sensing images, their
performance significantly deteriorates when there exists a large domain shift
between training and test data. To address this issue, we propose a new data
augmentation approach that transfers the style of test data to training data
using generative adversarial networks. Our semantic segmentation framework
consists in first training a U-net from the real training data and then
fine-tuning it on the test stylized fake training data generated by the
proposed approach. Our experimental results prove that our framework
outperforms the existing domain adaptation methods.
