## 2 Related Works  
In this section, we will discuss the works that treated domain adaptation within semantic segmentation. Generally, it is assumed, in the machine learning context, that the test and the training datasets are belonging to the same distribution. However, there is substantial discordance between them in real cases. This discordance reduces the model's efficiency out of its training domain. Domain adaptation techniques are used mitigate this discordance and to make the model generalizes better over multiple domains. In computer vision, efforts made on domain adaptation have been more focused on regression and classification tasks [21] not on semantic segmentation. For example, many works treated the training of the models on online images for the classification of objects in reality [22]. Recent works in this area are focusing on improving the adaptability of deep learning algorithms [15; 16; 23; 24; 25]. In semantic segmentation, most of the domain adaptation works are geared towards the use of simulated data [26; 27; 28; 29; 30; 31]. In fact, domain adaptation was expected to be used by these works for the improvement of the efficiency of segmentation on real images through the training of the semantic segmentation model on pure synthetic data. FCNs in the wild [32] is one of the earliest works. It uses a pixel-level adversarial loss to guide the model to learn the domain-invariant properties. This aims at making the adversarial classifier not to differentiate between the target and the source domains. The goal is to make its performance equal on the two domains. [PERSON] et al. [26] designed CyCADA, which is a model that changes synthetic data (source domain images) into real data style (target domain images) using CycleGAN. The segmentation model is then fed with the converted images to improve its accuracy on dealing with real photos. On the other hand, [PERSON] et al. [33] pointed out that a curriculum-style learning technique helps in reducing the domain shift. They deduced the target data properties by joining the learning of global label distribution with the learning of local distributions over landmark superpixels. The segmentation network was then trained through regularizing it to follow these features. [PERSON] et al. [34] treated the problem differently by taking the source and the target images as input to an auto-encoder network that processes and regenerates them before feeding them to the segmentation model. CGAN architecture is another approach introduced by [PERSON] et al. [35] that makes addition of random noise into the source data giving it as input to the segmentation model. They proved that this technique enhances the model's efficiency on the target domains. [PERSON] et al. [36] proposed to train independently two networks for the target and the source domains. The training of the target model is done through its regression into the source model weights since the target domain is without labels. It should be noted that the calculation of an adversarial loss in each layer of both networks is done. Aerial imagery has its peculiarities that should be considered. Therefore, a dedicated work that takes into consideration its peculiarities should be investigated. Recently, three works targeted the domain adaptation in semantic segmentation of aerial imagery. All of them used an unsupervised approach. The first work is the algorithm proposed by [PERSON] et al. [4]. They introduced a GAN architecture to map images in an unsupervised way from the source domain to the target domain without the need for paired data. After that, they translated the source domain dataset into the target domain. Then, the translated dataset is used to fine-tune the segmentation model to enhance its ability to treat images from the target domain. The second work is the work proposed by [PERSON] et al. [37]. They adopted the same algorithm of [PERSON] et al. [4] but changed the GAN architecture into another architecture named ColorMapGAN.