# Unsupervised Domain Adaptation using Generative Adversarial Networks for Semantic Segmentation of Aerial Images

arXiv: https://arxiv.org/abs/1905.03198 · HTML: https://mlsanity.com/paper/1905.03198/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2019.

## Built on by (most cited first, 30 of 32)

- [The Segment Anything Model (SAM) for Remote Sensing Applications: From Zero to One Shot](https://mlsanity.com/paper/2306.16623/) (2023)
- [Semantics for Robotic Mapping, Perception and Interaction: A Survey](https://mlsanity.com/paper/2101.00443/) (2021)
- [A Review on Deep Learning in UAV Remote Sensing](https://mlsanity.com/paper/2101.10861/) (2021)
- [AI Security for Geoscience and Remote Sensing: Challenges and Future Trends](https://mlsanity.com/paper/2212.09360/) (2022)
- [Domain-Adversarial Training of Self-Attention Based Networks for Land Cover Classification using Multi-temporal Sentinel-2 Satellite Imagery](https://mlsanity.com/paper/2104.00564/) (2021)
- [Decomposition-based Unsupervised Domain Adaptation for Remote Sensing Image Semantic Segmentation](https://mlsanity.com/paper/2404.04531/) (2024)
- [Curriculum-style Local-to-global Adaptation for Cross-domain Remote Sensing Image Segmentation](https://mlsanity.com/paper/2203.01539/) (2022)
- [FAR: Fourier Aerial Video Recognition](https://mlsanity.com/paper/2203.10694/) (2022)
- [Appearance Based Deep Domain Adaptation for the Classification of Aerial Images](https://mlsanity.com/paper/2108.07779/) (2021)
- [Multi-Class Segmentation from Aerial Views using Recursive Noise Diffusion](https://mlsanity.com/paper/2212.00787/) (2022)
- [Randomized Histogram Matching: A Simple Augmentation for Unsupervised Domain Adaptation in Overhead Imagery](https://mlsanity.com/paper/2104.14032/) (2021)
- [Birds of A Feather Flock Together: Category-Divergence Guidance for Domain Adaptive Segmentation](https://mlsanity.com/paper/2204.02111/) (2022)
- [Self-Training Guided Disentangled Adaptation for Cross-Domain Remote Sensing Image Semantic Segmentation](https://mlsanity.com/paper/2301.05526/) (2023)
- [Mind The Gap: Alleviating Local Imbalance for Unsupervised Cross-Modality Medical Image Segmentation](https://mlsanity.com/paper/2205.11888/) (2022)
- [IRSAMap:Towards Large-Scale, High-Resolution Land Cover Map Vectorization](https://mlsanity.com/paper/2508.16272/) (2025)
- [ResiDualGAN: Resize-Residual DualGAN for Cross-Domain Remote Sensing Images Semantic Segmentation](https://mlsanity.com/paper/2201.11523/) (2022)
- [Differentiable Frequency-based Disentanglement for Aerial Video Action Recognition](https://mlsanity.com/paper/2209.09194/) (2022)
- [DDF: A Novel Dual-Domain Image Fusion Strategy for Remote Sensing Image Semantic Segmentation with Unsupervised Domain Adaptation](https://mlsanity.com/paper/2403.02784/) (2024)
- [Unsupervised Domain Adaptation for Semantic Segmentation using One-shot Image-to-Image Translation via Latent Representation Mixing](https://mlsanity.com/paper/2212.03826/) (2022)
- [TAU: A Framework for Video-Based Traffic Analytics Leveraging Artificial Intelligence and Unmanned Aerial Systems](https://mlsanity.com/paper/2303.00337/) (2023)
- [Cross-Region Building Counting in Satellite Imagery using Counting Consistency](https://mlsanity.com/paper/2110.13558/) (2021)
- [Deep Learning Based Domain Adaptation Methods in Remote Sensing: A Comprehensive Survey](https://mlsanity.com/paper/2510.15615/) (2025)
- [Depth-Assisted ResiDualGAN for Cross-Domain Aerial Images Semantic Segmentation](https://mlsanity.com/paper/2208.09823/) (2022)
- [Exploration of Convolutional Neural Network Architectures for Large Region Map Automation](https://mlsanity.com/paper/2211.03854/) (2022)
- [Leveraging Topology for Domain Adaptive Road Segmentation in Satellite and Aerial Imagery](https://mlsanity.com/paper/2309.15625/) (2023)
- [Cross-dataset domain adaptation for the classification COVID-19 using chest computed tomography images](https://mlsanity.com/paper/2311.08524/) (2023)
- [Joint-Optimized Unsupervised Adversarial Domain Adaptation in Remote Sensing Segmentation with Prompted Foundation Model](https://mlsanity.com/paper/2411.05878/) (2024)
- [Source-free domain adaptation based on label reliability for cross-domain bearing fault diagnosis](https://mlsanity.com/paper/2503.08749/) (2025)
- [Self-Supervised Pretraining for Aerial Road Extraction](https://mlsanity.com/paper/2503.24326/) (2025)
- [Generalization-aware Remote Sensing Change Detection via Domain-agnostic Learning](https://mlsanity.com/paper/2504.00543/) (2025)

## Abstract (arXiv)

Segmenting aerial images is being of great potential in surveillance and scene understanding of urban areas. It provides a mean for automatic reporting of the different events that happen in inhabited areas. This remarkably promotes public safety and traffic management applications. After the wide adoption of convolutional neural networks methods, the accuracy of semantic segmentation algorithms could easily surpass 80% if a robust dataset is provided. Despite this success, the deployment of a pre-trained segmentation model to survey a new city that is not included in the training set significantly decreases the accuracy. This is due to the domain shift between the source dataset on which the model is trained and the new target domain of the new city images. In this paper, we address this issue and consider the challenge of domain adaptation in semantic segmentation of aerial images. We design an algorithm that reduces the domain shift impact using Generative Adversarial Networks (GANs). In the experiments, we test the proposed methodology on the International Society for Photogrammetry and Remote Sensing (ISPRS) semantic segmentation dataset and found that our method improves the overall accuracy from 35% to 52% when passing from Potsdam domain (considered as source domain) to Vaihingen domain (considered as target domain). In addition, the method allows recovering efficiently the inverted classes due to sensor variation. In particular, it improves the average segmentation accuracy of the inverted classes due to sensor variation from 14% to 61%.