NDLI: Learning deep representations for ground-to-aerial geolocalization

Content Provider	IEEE Xplore Digital Library
Author	Tsung-Yi Lin Yin Cui Belongie, S. Hays, J.
Copyright Year	2015
Description	Author affiliation: Cornell Tech, Cornell Univ., Ithaca, NY, USA (Tsung-Yi Lin; Yin Cui; Belongie, S.) \|\| Brown Univ., Providence, RI, USA (Hays, J.)
Abstract	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.
Starting Page	5007
Ending Page	5015
File Size	3193600
Page Count	9
File Format	PDF
ISSN	10636919
e-ISBN	9781467369640
DOI	10.1109/CVPR.2015.7299135
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Geology Yttrium Training
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Cross-View Image Geolocalization

Learning hierarchical representations for face verification with convolutional deep belief networks

Aerial Imagery Registration Using Deep Learning for UAV Geolocalization

Deep correlation for matching images and text

Deep transfer metric learning

Cross-View Image Retrieval -- Ground to Aerial Image Retrieval through Deep Learning

Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks

Video event recognition with deep hierarchical context model

Geometric Urban Geo-localization

Learning deep representations for ground-to-aerial geolocalization

Similar Documents

Cross-View Image Geolocalization

Learning hierarchical representations for face verification with convolutional deep belief networks

Aerial Imagery Registration Using Deep Learning for UAV Geolocalization

Deep correlation for matching images and text

Deep transfer metric learning

Cross-View Image Retrieval -- Ground to Aerial Image Retrieval through Deep Learning

Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks

Video event recognition with deep hierarchical context model

Geometric Urban Geo-localization

Learning deep representations for ground-to-aerial geolocalization