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

## TL;DR

This paper presents a GAN-based unsupervised domain adaptation method to improve semantic segmentation accuracy of aerial images across different cities, addressing domain shift issues.

## Contribution

It introduces a novel GAN-based algorithm for unsupervised domain adaptation in aerial image segmentation, significantly enhancing cross-city accuracy.

## Key findings

- Improves overall accuracy from 35% to 52% across domains.
- Recovers inverted classes due to sensor variation, increasing accuracy from 14% to 61%.
- Demonstrates effectiveness on ISPRS dataset.

## Abstract

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%.