  Reliable image geolocation is crucial for several applications, ranging from
social media geo-tagging to fake news detection. State-of-the-art geolocation
methods surpass human performance on the task of geolocation estimation from
images. However, no method assesses the suitability of an image for this task,
which results in unreliable and erroneous estimations for images containing no
geolocation clues. In this paper, we define the task of image localizability,
i.e. suitability of an image for geolocation, and propose a selective
prediction methodology to address the task. In particular, we propose two novel
selection functions that leverage the output probability distributions of
geolocation models to infer localizability at different scales. Our selection
functions are benchmarked against the most widely used selective prediction
baselines, outperforming them in all cases. By abstaining from predicting
non-localizable images, we improve geolocation accuracy from 27.8% to 70.5% at
the city-scale, and thus make current geolocation models reliable for
real-world applications.
