Enhancing Worldwide Image Geolocation by Ensembling Satellite-Based Ground-Level Attribute Predictors

Bianco Michael, David Eigen, Michael Gormish; Proceedings of the Winter Conference on Applications of Computer Vision (WACV) Workshops, 2025, pp. 572-580

We examine the challenge of estimating the location of a single ground-level image in the absence of GPS or other location metadata. Geolocation systems are typically evaluated by measuring the Great Circle Distance between a single predicted location and the ground truth. Because this measurement only uses a single point it cannot assess the quality of an estimated set of regions or score heatmaps. It is critical to characterize the distribution of potential geolocation areas to help when a system is applied to less well-sampled regions such as rural and wilderness areas where finding the exact location may not be possible. This evaluation may help characterize a system in relation to follow-on procedures that further narrow down or verify predicted locations. This paper introduces a novel metric Recall vs Area (RvA) which assesses distributions of location estimates. RvA treats image geolocation results in a manner similar to precision-recall in document retrieval measuring recall as a function of area. For an ordered 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 analogous to precision-recall enabling evaluation for varying search budgets. This view of the problem inspires a simple ensembling approach to global-scale image geolocation that combines multiple models attribute predictors and data sources. Specifically we combine the geolocation models GeoEstimation and GeoCLIP with attribute predictors based on ORNL LandScan and ESA Land Cover. We find notable improvements in non-urban and under-represented areas on Im2GPS3k and Street View datasets.