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Image geolocalization, inferring the geographic location of an image, is achallenging computer vision problem with many potential applications. Therecent state-of-the-art approach to this problem is a deep image classificationapproach in which the world is spatially divided into cells and a deep networkis trained to predict the correct cell for a given image. We propose to combinethis approach with the original Im2GPS approach in which a query image ismatched against a database of geotagged images and the location is inferredfrom the retrieved set. We estimate the geographic location of a query image byapplying kernel density estimation to the locations of its nearest neighbors inthe reference database. Interestingly, we find that the best features for ourretrieval task are derived from networks trained with classification loss eventhough we do not use a classification approach at test time. Training withclassification loss outperforms several deep feature learning methods (e.g.Siamese networks with contrastive of triplet loss) more typical for retrievalapplications. Our simple approach achieves state-of-the-art geolocalizationaccuracy while also requiring significantly less training data.

Revisiting IM2GPS in the Deep Learning Era