Many applications in Artificial Intelligence require the capability of an autonomous system to accurately and efficiently locate itself in the real world. The task of Visual Geo-localization (VG) can be formulated as the ability to recognize the geographical location of a picture, using only its visual information and comparing it to a database of geotagged images, which represent the previously visited places or the area under analysis. In the last two decades, this field has seen rapid growth in interest and technical development from different communities. Consequently, the research landscape has become increasingly fragmented and dissociated. The first half of this thesis work consists of an extensive survey of Deep Learning methods and the development of a benchmarking framework.