  The image-to-GPS verification problem asks whether a given image is taken at
a claimed GPS location. In this paper, we treat it as an image verification
problem -- whether a query image is taken at the same place as a reference
image retrieved at the claimed GPS location. We make three major contributions:
1) we propose a novel custom bottom-up pattern matching (BUPM) deep neural
network solution; 2) we demonstrate that the verification can be directly done
by cross-checking a perspective-looking query image and a panorama reference
image, and 3) we collect and clean a dataset of 30K pairs query and reference.
Our experimental results show that the proposed BUPM solution outperforms the
state-of-the-art solutions in terms of both verification and localization.
