  Estimating the location where an image was taken based solely on the contents
of the image is a challenging task, even for humans, as properly labeling an
image in such a fashion relies heavily on contextual information, and is not as
simple as identifying a single object in the image. Thus any methods which
attempt to do so must somehow account for these complexities, and no single
model to date is completely capable of addressing all challenges. This work
contributes to the state of research in image geolocation inferencing by
introducing a novel global meshing strategy, outlining a variety of training
procedures to overcome the considerable data limitations when training these
models, and demonstrating how incorporating additional information can be used
to improve the overall performance of a geolocation inference model. In this
work, it is shown that Delaunay triangles are an effective type of mesh for
geolocation in relatively low volume scenarios when compared to results from
state of the art models which use quad trees and an order of magnitude more
training data. In addition, the time of posting, learned user albuming, and
other meta data are easily incorporated to improve geolocation by up to 11% for
country-level (750 km) locality accuracy to 3% for city-level (25 km)
localities.
