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Although convolutional neural networks have been proven to be an effectivetool to generate high quality maps from remote sensing images, theirperformance significantly deteriorates when there exists a large domain shiftbetween training and test data. To address this issue, we propose a new dataaugmentation approach that transfers the style of test data to training datausing generative adversarial networks. Our semantic segmentation frameworkconsists in first training a U-net from the real training data and thenfine-tuning it on the test stylized fake training data generated by theproposed approach. Our experimental results prove that our frameworkoutperforms the existing domain adaptation methods.

SemI2I: Semantically Consistent Image-to-Image Translation for Domain Adaptation of Remote Sensing Data