  We address the problem of ground-to-satellite image geo-localization, that
is, estimating the camera latitude, longitude and orientation (azimuth angle)
by matching a query image captured at the ground level against a large-scale
database with geotagged satellite images. Our prior arts treat the above task
as pure image retrieval by selecting the most similar satellite reference image
matching the ground-level query image. However, such an approach often produces
coarse location estimates because the geotag of the retrieved satellite image
only corresponds to the image center while the ground camera can be located at
any point within the image. To further consolidate our prior research findings,
we present a novel geometry-aware geo-localization method. Our new method is
able to achieve the fine-grained location of a query image, up to pixel size
precision of the satellite image, once its coarse location and orientation have
been determined. Moreover, we propose a new geometry-aware image retrieval
pipeline to improve the coarse localization accuracy. Apart from a polar
transform in our conference work, this new pipeline also maps satellite image
pixels to the ground-level plane in the ground-view via a geometry-constrained
projective transform to emphasize informative regions, such as road structures,
for cross-view geo-localization. Extensive quantitative and qualitative
experiments demonstrate the effectiveness of our newly proposed framework. We
also significantly improve the performance of coarse localization results
compared to the state-of-the-art in terms of location recalls.
