In the field of Deep Learning, and more specifically Computer Vision, one of the tasks that covers quite a lot of interest is that of Super-Resolution: a set of techniques used to improve the resolution of digital images. Super-Resolution techniques have found room for application in many fields, among which one of the most interesting is that of remote sensing and earth observation; this is amply evidenced by the very numerous challenges on the subject organized by space entities such as ESA and NASA. Being able to improve the spatial resolution, i.e. the physical measurement (meters) that represents the size of a pixel, of a satellite image can be particularly useful for a number of reasons including being able to make object classification and detection tasks easier to solve, or again, to monitor at a greater level of detail the earth's surface.