  Geolocating images of a ground-level scene entails estimating the location on
Earth where the picture was taken, in absence of GPS or other location
metadata. Typically, methods are evaluated by measuring the Great Circle
Distance (GCD) between a predicted location and ground truth. However, this
measurement is limited because it only evaluates a single point, not estimates
of regions or score heatmaps. This is especially important in applications to
rural, wilderness and under-sampled areas, where finding the exact location may
not be possible, and when used in aggregate systems that progressively narrow
down locations.
  In this paper, we introduce a novel metric, Recall vs Area (RvA), which
measures the accuracy of estimated distributions of locations. RvA treats image
geolocation results similarly to document retrieval, measuring recall as a
function of area: For a ranked list of (possibly non-contiguous) predicted
regions, we measure the accumulated area required for the region to contain the
ground truth coordinate. This produces a curve similar to a precision-recall
curve, where "precision" is replaced by square kilometers area, allowing
evaluation of performance for different downstream search area budgets.
  Following directly from this view of the problem, we then examine a simple
ensembling approach to global-scale image geolocation, which incorporates
information from multiple sources to help address domain shift, and can readily
incorporate multiple models, attribute predictors, and data sources. We study
its effectiveness by combining the geolocation models GeoEstimation and the
current SOTA GeoCLIP, with attribute predictors based on ORNL LandScan and
ESA-CCI Land Cover. We find significant improvements in image geolocation for
areas that are under-represented in the training set, particularly non-urban
areas, on both Im2GPS3k and Street View images.
