Domain Adaptation (DA) is the task of making a model function effectively on domains that differ from the one(s) it was trained on. Visual (Geo-)Localization, on the other hand, is the task of estimating the position or the pose in which a query image has been taken by comparing it to a large labeled database. The objective of this thesis is to investigate two scenarios where this paradigm is deployed in challenging domains which, without proper countermeasures, would cause severe degradation in performances. In particular, the first one regards the deployment of a large scale model in a different city from the one that it has been trained on.