Wide-Area Image Geolocalization With Aerial Reference Imagery

Scott Workman, Richard Souvenir, Nathan Jacobs; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2015, pp. 3961-3969

Abstract


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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[bibtex]
@InProceedings{Workman_2015_ICCV,
author = {Workman, Scott and Souvenir, Richard and Jacobs, Nathan},
title = {Wide-Area Image Geolocalization With Aerial Reference Imagery},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2015}
}