This thesis is situated at the overlap of probabilistic machine learning and remote sensing as it analyses the application of variational auto-encoders to satellite images of fields with the final objective of image classification. The rising availability of high-resolution satellite images of fields increases the need for compressing the images in order to keep maintenance and inference of machine learning models on a feasible and cost-efficient scale. Machine learning, in general, has proven to offer auspicious methods for summarising high-dimensional data into a lower-dimensional representation. Variational auto-encoders are a modern and advanced representation learning algorithm and are the topic of research in this thesis. An extensive hyperparameter search for the implemented networks is performed. The best architecture is selected and compared against conventional computer vision methods. The research shows that summarising high-resolution satellite images with variational auto-encoders is possible. It will, however, still take a performance hit on the classification tasks in comparison to the conventional computer vision techniques. The findings show the potential that variational auto-encoders offer for image compression but also that the used method needs further refinement in order to beat conventional approaches
