# SemI2I: Semantically Consistent Image-to-Image Translation for Domain Adaptation of Remote Sensing Data Source: [SemI2I: Semantically Consistent Image-to-Image Translation for Domain Adaptation of Remote Sensing Data]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:07:19.924Z Authors: Onur Tasar, S L Happy, Yuliya Tarabalka, Pierre Alliez Published: 2020-02-14T09:07:09.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()