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Wide-area image geolocalization with aerial reference imagery
Workman, S Souvenir, R Jacobs, N
Workman, S
Souvenir, R
Jacobs, N
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Conference Proceeding
Date
2015-02-17
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10.1109/ICCV.2015.451
Abstract
© 2015 IEEE. 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 aerial images at multiple spatial scales. To support training these networks, we introduce a massive database that contains pairs of aerial and ground-level images from across the United States. Our methods significantly out-perform the state of the art on two benchmark datasets. We also show, qualitatively, that the proposed feature representations are discriminative at both local and continental spatial scales.
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Proceedings of the IEEE International Conference on Computer Vision
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