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We propose to use deep convolutional neural networks to address the problem of cross-view image geolocalization, in which the geolocation of a ground-level query image is estimated by matching to georeferenced aerial images. We use state-of-the-art feature representations for ground-level images and introduce a cross-view training approach for learning a joint semantic feature representation for aerial images. We also propose a network architecture that fuses features extracted from aerialdoi:10.1109/iccv.2015.451 dblp:conf/iccv/WorkmanSJ15 fatcat:mbxc3menl5gstn45lqw6xupgjq