Scaling Image Geo-Localization to Continent Level
Philipp Lindenberger, Eduard Trulls, Paul-Edouard Sarlin, Jan Hosang, Marc Pollefeys, Simon Lynen
Determining the precise geographic location of an image at a global scaleremains an unsolved challenge. Standard image retrieval techniques are simply not applicable, due to the sheer volume of images (>100M).Scalable solutions, however, involve a trade-off:global classification typically yields coarse results (10+ kilometers), while cross-view retrieval between ground and aerial imagery suffers from a domain gap and has been primarily studied on smaller geographic regions.This paper introduces a novel hybrid approach that achieves fine-grained geo-localization across a large geographic expanse at the scale of a continent.We leverage a proxy classification task during training to learn rich feature representations that implicitly encode precise location information.We combine these learned prototypes with embeddings of aerial imagery to increase robustness to the sparsity of ground-level data.This enables direct, fine-grained retrieval over areas spanning multiple countries.Our extensive evaluationdemonstrates that our approach can achieve >50% top-1 recall at 100 meters over a substantial part of Europe.