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    Wide-area image geolocalization with aerial reference imagery

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    Genre
    Conference Proceeding
    Date
    2015-02-17
    Author
    Workman, S
    Souvenir, R
    Jacobs, N
    Subject
    cs.CV
    cs.CV
    Permanent link to this record
    http://hdl.handle.net/20.500.12613/5818
    
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    DOI
    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.
    Citation to related work
    IEEE
    Has part
    Proceedings of the IEEE International Conference on Computer Vision
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    ae974a485f413a2113503eed53cd6c53
    http://dx.doi.org/10.34944/dspace/5800
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