  We propose a method for accurately localizing ground vehicles with the aid of
satellite imagery. Our approach takes a ground image as input, and outputs the
location from which it was taken on a georeferenced satellite image. We perform
visual localization by estimating the co-occurrence probabilities between the
ground and satellite images based on a ground-satellite feature dictionary. The
method is able to estimate likelihoods over arbitrary locations without the
need for a dense ground image database. We present a ranking-loss based
algorithm that learns location-discriminative feature projection matrices that
result in further improvements in accuracy. We evaluate our method on the
Malaga and KITTI public datasets and demonstrate significant improvements over
a baseline that performs exhaustive search.
