Learning deep representations for ground-to-aerial geolocalization
Research output: Contribution to journal › Conference article › Research › peer-review
The recent availability of geo-tagged images and rich geospatial data has inspired a number of algorithms for image based geolocalization. Most approaches predict the location of a query image by matching to ground-level images with known locations (e.g., street-view data). However, most of the Earth does not have ground-level reference photos available. Fortunately, more complete coverage is provided by oblique aerial or 'bird's eye' imagery. In this work, we localize a ground-level query image by matching it to a reference database of aerial imagery. We use publicly available data to build a dataset of 78K aligned crossview image pairs. The primary challenge for this task is that traditional computer vision approaches cannot handle the wide baseline and appearance variation of these cross-view pairs. We use our dataset to learn a feature representation in which matching views are near one another and mismatched views are far apart. Our proposed approach, Where-CNN, is inspired by deep learning success in face verification and achieves significant improvements over traditional hand-crafted features and existing deep features learned from other large-scale databases. We show the effectiveness of Where-CNN in finding matches between street view and aerial view imagery and demonstrate the ability of our learned features to generalize to novel locations.
Original language | English |
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Journal | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
Pages (from-to) | 5007-5015 |
Number of pages | 9 |
ISSN | 1063-6919 |
DOIs | |
Publication status | Published - 14 Oct 2015 |
Externally published | Yes |
Event | IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015 - Boston, United States Duration: 7 Jun 2015 → 12 Jun 2015 |
Conference
Conference | IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015 |
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Country | United States |
City | Boston |
Period | 07/06/2015 → 12/06/2015 |
Bibliographical note
Publisher Copyright:
© 2015 IEEE.
ID: 301829041