## 1 Introduction  
The semantic segmentation task provides for every pixel in the input image a label that defines its semantic class. In remote sensing context, the semantic segmentation of aerial images has an increasing potential for many tasks and applications, like analysis and management of road traffic, monitoring of urban and rural areas, fast interactions in case of emergency, and so on. The growing adoption of Unmanned Aerial Vehicles (UAVs) is behind this increasing potential. High-resolution images can be collected using UAVs from different points of view and processed by the semantic segmentation algorithms to promote the automatic analysis of surveyed scenes. Since the emergence of Convolutional Neural Networks (CNNs), the area of image analysis algorithms has shown a considerable improvement in accuracy [1; 2; 3; 4; 5; 6; 7]. This affected directly the area of semantic segmentation and paved the way towards a variety of CNN-based architectures, like SegNet [8], fully connected network (FCN), PSPNet [9], U-Net [10] and DeepLab [11]. Empirically, if we build a robust dataset and we train on it one of the state-of-the-art algorithms, we will get an accuracy that easily surpasses 80%. Despite this exciting efficiency, a notable challenge is blocking the use of semantic segmentation algorithms in real cases. In fact, the accuracy of the model will be high only on images belonging to the same domain of the dataset used in training (object representation, resolution, sensor type, lighting conditions). If we test this model in another domain (images collected under conditions different from those of the training set), the accuracy decreases remarkably. This decrease is caused by the domain shift existing between the target and the source domain. Figure 1 shows a real case scenario in which we want to test the semantic segmentation model on a domain different from the source. A considerable shift is remarked between the two domains (location is changed, image coding is changed, resolution is changed). The straightforward solution to mitigate this intriguing limitation is to train our model on a labeled dataset constructed from the target domain in a supervised or a semi-supervised way [12]. However, this method is costly and time-consuming. To illustrate this, we can note that pixel-labeling of an image from Cityscapes dataset takes nearly 90 minutes on average [13]. The size of the image is 2040 by 1016 pixels. To reduce the labeling time, we can benefit from human crowd intelligence by distributing the labeling task over a set of human crowds [14]. Every set will be allocated to the labeling of one class. This reduces the cost of generating the semantic labeled dataset on the target. Nevertheless, since such solutions are not always immediately available, it is still useful to search for a data-efficient solution of domain adaptation that uses only a minimal set of images for supervision of the domain transfer. Domain adaptation is a separate area in machine learning whose objective is to learn how a model performance on a source data distribution can be improved on another target data distribution. Domain adaptation helps in mitigating the domain shift between the target domain data and the source domain data used in training. Typically, a mapping function is designed between the target domain and the source domain. Deep learning models have been used by the recent methods of domain adaptation for the training of such a mapping function [15; 16; 17; 18]. Motivated by the current breakthrough made by GANs (Generative Adversarial Networks) [19; 20], we developed a data-efficient domain adaptation algorithm based upon an architecture of two GAN networks. Our method aims at handling the case indicated in Figure 1 as well as other cases of the same nature. Our technique is based on converting the image that we want to segment from the  
Figure 1: Cross-domains semantic segmentation of aerial imagery. target domain to the source domain by passing it through two consecutive GAN networks.