  Planet-scale photo geolocalization is the complex task of estimating the
location depicted in an image solely based on its visual content. Due to the
success of convolutional neural networks (CNNs), current approaches achieve
super-human performance. However, previous work has exclusively focused on
optimizing geolocalization accuracy. Due to the black-box property of deep
learning systems, their predictions are difficult to validate for humans.
State-of-the-art methods treat the task as a classification problem, where the
choice of the classes, that is the partitioning of the world map, is crucial
for the performance. In this paper, we present two contributions to improve the
interpretability of a geolocalization model: (1) We propose a novel semantic
partitioning method which intuitively leads to an improved understanding of the
predictions, while achieving state-of-the-art results for geolocational
accuracy on benchmark test sets; (2) We introduce a metric to assess the
importance of semantic visual concepts for a certain prediction to provide
additional interpretable information, which allows for a large-scale analysis
of already trained models. Source code and dataset are publicly available.
