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In this paper, we address the problem of cross-view image geo-localization.Specifically, we aim to estimate the GPS location of a query street view imageby finding the matching images in a reference database of geo-tagged bird's eyeview images, or vice versa. To this end, we present a new framework forcross-view image geo-localization by taking advantage of the tremendous successof deep convolutional neural networks (CNNs) in image classification and objectdetection. First, we employ the Faster R-CNN to detect buildings in the queryand reference images. Next, for each building in the query image, we retrievethe $k$ nearest neighbors from the reference buildings using a Siamese networktrained on both positive matching image pairs and negative pairs. To find thecorrect NN for each query building, we develop an efficient multiple nearestneighbors matching method based on dominant sets. We evaluate the proposedframework on a new dataset that consists of pairs of street view and bird's eyeview images. Experimental results show that the proposed method achieves bettergeo-localization accuracy than other approaches and is able to generalize toimages at unseen locations.

Cross-View Image Matching for Geo-localization in Urban Environments