Semantic segmentation is the task of detecting and classifying each pixel of an image with a semantic class (i.e. road, sidewalk, pedestrian, etc). It is a powerful tool for various applications, ranging from robotics to aerial image analysis, but its primary application is in autonomous driving, where recognizing the entire scene is critical to making the best driving decision. One of the major challenges with semantic segmentation is the shortage of large pixel-by-pixel annotated datasets, which are costly in terms of both human and financial resources. A common approach is to train models using synthetic datasets composed of simulated images and pixel-accurate ground truth labels, and then utilize Domain Adaptation (DA) approaches to bridge the gap between such artificial domains (referred to as source) and the real ones (to which we refer as target).