Wide-area image geolocalization with aerial reference imagery
dc.creator | Workman, S | |
dc.creator | Souvenir, R | |
dc.creator | Jacobs, N | |
dc.date.accessioned | 2021-02-03T18:06:32Z | |
dc.date.available | 2021-02-03T18:06:32Z | |
dc.date.issued | 2015-02-17 | |
dc.identifier.issn | 1550-5499 | |
dc.identifier.doi | http://dx.doi.org/10.34944/dspace/5800 | |
dc.identifier.other | BF1NZ (isidoc) | |
dc.identifier.uri | http://hdl.handle.net/20.500.12613/5818 | |
dc.description.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. | |
dc.format.extent | 3961-3969 | |
dc.relation.haspart | Proceedings of the IEEE International Conference on Computer Vision | |
dc.relation.isreferencedby | IEEE | |
dc.subject | cs.CV | |
dc.subject | cs.CV | |
dc.title | Wide-area image geolocalization with aerial reference imagery | |
dc.type | Article | |
dc.type.genre | Conference Proceeding | |
dc.relation.doi | 10.1109/ICCV.2015.451 | |
dc.ada.note | For Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu | |
dc.creator.orcid | Souvenir, Richard|0000-0002-6066-0946 | |
dc.date.updated | 2021-02-03T18:06:28Z | |
refterms.dateFOA | 2021-02-03T18:06:33Z |