Image super-resolution is a widely studied ill-posed problem in computer vision, where the objective is to convert a low-resolution image to a high-resolution one. Conventional methods for achieving super-resolution, such as interpolation-based methods, require a lot of pre/post-processing and optimization. Thanks to the rise in popularity of Deep Learning methods over recent years, several studies have shown how learning methods such as convolutional neural networks and generative adversarial networks can be used to perform super-resolution tasks with competitive results when compared to prior state of the art methods. This thesis proposes a focus on the application of super-resolution methods to open-source low-resolution satellite imagery gathered from the Sentinel-2 ESA’s satellite in the RGB domain.