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

arXiv: https://arxiv.org/abs/2002.05925 · HTML: https://mlsanity.com/paper/2002.05925/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2020.

## Built on by (most cited first, 4 of 4)

- [Appearance Based Deep Domain Adaptation for the Classification of Aerial Images](https://mlsanity.com/paper/2108.07779/) (2021)
- [Randomized Histogram Matching: A Simple Augmentation for Unsupervised Domain Adaptation in Overhead Imagery](https://mlsanity.com/paper/2104.14032/) (2021)
- [High-resolution semantically-consistent image-to-image translation](https://mlsanity.com/paper/2209.06264/) (2022)
- [Multi-Sensor Diffusion-Driven Optical Image Translation for Large-Scale Applications](https://mlsanity.com/paper/2404.11243/) (2024)

## Abstract (arXiv)

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.