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Localizing and Orienting Street Views Using Overhead Imagery [article]

Nam Vo, James Hays
2017 arXiv   pre-print
This image matching task is challenging not just because of the dramatic viewpoint difference between ground-level and overhead imagery but because the orientation (i.e. azimuth) of the street views is  ...  For this task we collect a new dataset with one million pairs of street view and overhead images sampled from eleven U.S. cities.  ...  Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied  ... 
arXiv:1608.00161v2 fatcat:ost7yhemgzalvio643tw4322eq

Localizing and Orienting Street Views Using Overhead Imagery [chapter]

Nam N. Vo, James Hays
2016 Lecture Notes in Computer Science  
overhead image, which is achieved by:  ...  Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied  ... 
doi:10.1007/978-3-319-46448-0_30 fatcat:aajds24ukjdwxizgwt5m6f5mva

Guest Editorial: Large Scale Visual Media Geo-Localization

Riad I. Hammoud, Josef Sivic, Larry S. Davis, Marc Pollefeys
2015 International Journal of Computer Vision  
This area of research encompasses many disciplines, including pre-processing and processing earth-scale overhead and ground-level reference imagery (i.e. satellite, elevation maps, LIDAR, open-street maps  ...  ) reference imageries, satellite imagery segmentation, low-level and high-level feature extraction, raytracing and synthesized imagery generation, wide imagery registration, multi-view geometry and 3D  ...  The second paper is on "Geo-localization using Volumetric Representations of Overhead Imagery" (doi:10.1007/ s11263-015-0850-9) by Ozge C. Ozcanli, Yi Dong and Joseph L. Mundy.  ... 
doi:10.1007/s11263-015-0870-5 fatcat:fkerrxcxnjglxoehj35tcl25ge

Toward Seamless Multiview Scene Analysis From Satellite to Street Level

Sebastien Lefevre, Devis Tuia, Jan Dirk Wegner, Timothee Produit, Ahmed Samy Nassar
2017 Proceedings of the IEEE  
In this paper, we discuss and review how combined multi-view imagery from satellite to street-level can benefit scene analysis.  ...  What makes the combination of overhead and street-level images challenging, is the strongly varying viewpoint, different scale, illumination, sensor modality and time of acquisition.  ...  Multimodal data fusion The previous two sections reviewed localization and matching by joint use of overhead and ground imagery.  ... 
doi:10.1109/jproc.2017.2684300 fatcat:r3hyfdtjzzaknl25cpygxrcgq4

Geo-localization of street views with aerial image databases

Mayank Bansal, Harpreet S. Sawhney, Hui Cheng, Kostas Daniilidis
2011 Proceedings of the 19th ACM international conference on Multimedia - MM '11  
; and (3) Position and orientation estimation of ground images.  ...  As a result, localization of ground level imagery with respect to aerial collections is a technically important and practically significant problem.  ...  For the test imagery, we used imagery from Panoramio and screen-shots from Google Street-view both of which contain lat-long information.  ... 
doi:10.1145/2072298.2071954 dblp:conf/mm/BansalSCD11 fatcat:5amrmgx2sfeehnfex27tilm4li

Learning deep representations for ground-to-aerial geolocalization

Tsung-Yi Lin, Yin Cui, Serge Belongie, James Hays
2015 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
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.  ...  We use our dataset to learn a feature representation in which matching views are near one another and mismatched views are far apart.  ...  Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied  ... 
doi:10.1109/cvpr.2015.7299135 dblp:conf/cvpr/LinCBH15 fatcat:cjcqvosu45azti4n4g6zvawusu

Overhead-Based Image and Video Geo-localization Framework

Riad I. Hammoud, Scott A. Kuzdeba, Brian Berard, Victor Tom, Richard Ivey, Renu Bostwick, Jason HandUber, Lori Vinciguerra, Nathan Shnidman, Byron Smiley
2013 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops  
This paper presents a geo-localization framework of street-level outdoor images using multiple sources of overhead reference imagery including LIDAR, Digital Elevation Maps and Multi-Spectral Land Cover  ...  /Use imagery.  ...  "Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the view of United States Air Force."  ... 
doi:10.1109/cvprw.2013.55 dblp:conf/cvpr/HammoudKBTIBHVSS13 fatcat:jdv5olcbgfdgjcock63hntqtnm

A Unified Model for Near and Remote Sensing

Scott Workman, Menghua Zhai, David J. Crandall, Nathan Jacobs
2017 2017 IEEE International Conference on Computer Vision (ICCV)  
To evaluate our approach, we created a large dataset of overhead and ground-level images from a major urban area with three sets of labels: land use, building function, and building age.  ...  In our approach, we combine overhead and ground-level images in an end-toend trainable neural network, which uses kernel regression and density estimation to convert features extracted from the ground-level  ...  Acknowledgments We gratefully acknowledge the support of NSF CAREER grants IIS-1553116 (Jacobs) and IIS-1253549 (Crandall), a Google Faculty Research Award (Jacobs), and an equipment donation from IBM  ... 
doi:10.1109/iccv.2017.293 dblp:conf/iccv/WorkmanZCJ17 fatcat:3c2fcr5hnjazbepiqlwa5aqvte

A Unified Model for Near and Remote Sensing [article]

Scott Workman and Menghua Zhai and David J. Crandall and Nathan Jacobs
2017 arXiv   pre-print
To evaluate our approach, we created a large dataset of overhead and ground-level images from a major urban area with three sets of labels: land use, building function, and building age.  ...  In our approach, we combine overhead and ground-level images in an end-to-end trainable neural network, which uses kernel regression and density estimation to convert features extracted from the ground-level  ...  Acknowledgments We gratefully acknowledge the support of NSF CAREER grants IIS-1553116 (Jacobs) and IIS-1253549 (Crandall), a Google Faculty Research Award (Jacobs), and an equipment donation from IBM  ... 
arXiv:1708.03035v1 fatcat:44rhyygfgffy3oz2s76bzzuts4

A Survey of Deep Learning-based Object Detection Methods and Datasets for Overhead Imagery

Junhyung Kang, Shahroz Tariq, Han Oh, Simon S. Woo
2022 IEEE Access  
We thank Jin Yong Park for reviewing the earlier version of the draft, and providing helpful and insightful comments.  ...  E-SVMs and NDFT are the most frequently used methods. [71] used information obtained from multiple views such as street and overhead view.  ...  The Faster R-CNN model was utilized as a base detection model to detect objects from each street view image.  ... 
doi:10.1109/access.2022.3149052 fatcat:iwvyg7qf6jgntgrwk4bmonrd5m

Accurate Georegistration of Point Clouds Using Geographic Data

Chun-Po Wang, Kyle Wilson, Noah Snavely
2013 2013 International Conference on 3D Vision  
To address this problem, we propose a system for aligning 3D structure-from-motion point clouds, produced from Internet imagery, to existing geographic information sources, including Google Street View  ...  The Internet contains a wealth of rich geographic information about our world, including 3D models, street maps, and many other data sources.  ...  Acknowledgements This work was supported by the NSF under grants IIS-1149393 and IIS-1111534, and by Intel Corporation, Google, and Microsoft.  ... 
doi:10.1109/3dv.2013.13 dblp:conf/3dim/WangWS13 fatcat:quoeneh5jnahfjkrsgu7vl4lye

Cross-View Image Geo-localization [chapter]

Tsung-Yi Lin, Serge Belongie, James Hays
2016 Advances in Computer Vision and Pattern Recognition  
While there are many geotagged images available on photo sharing and street view sites, most are clustered around landmarks and urban areas.  ...  On the other hand, there is no shortage of visual and geographic data that densely covers the Earthwe examine overhead imagery and land cover survey data -but the relationship between this data and ground  ...  Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied  ... 
doi:10.1007/978-3-319-25781-5_4 fatcat:wspw6crodbbkdjuhjbj446t73u

Cross-View Image Geolocalization

Tsung-Yi Lin, Serge Belongie, James Hays
2013 2013 IEEE Conference on Computer Vision and Pattern Recognition  
While there are many geotagged images available on photo sharing and street view sites, most are clustered around landmarks and urban areas.  ...  On the other hand, there is no shortage of visual and geographic data that densely covers the Earthwe examine overhead imagery and land cover survey data -but the relationship between this data and ground  ...  Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied  ... 
doi:10.1109/cvpr.2013.120 dblp:conf/cvpr/LinBH13 fatcat:dqftuzefnnew5h2ov57xht4xbi

Cross-View Image Matching for Geo-Localization in Urban Environments

Yicong Tian, Chen Chen, Mubarak Shah
2017 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
We evaluate the proposed framework on a new dataset that consists of pairs of street view and bird's eye view images.  ...  In this paper, we address the problem of cross-view image geo-localization.  ...  The street view images are from Google and the overhead 45 • bird's eye view images are from Bing.  ... 
doi:10.1109/cvpr.2017.216 dblp:conf/cvpr/TianCS17 fatcat:qoz4ssykqfemvjzulgclmibozy

Cross-View Image Matching for Geo-localization in Urban Environments [article]

Yicong Tian and Chen Chen and Mubarak Shah
2017 arXiv   pre-print
We evaluate the proposed framework on a new dataset that consists of pairs of street view and bird's eye view images.  ...  In this paper, we address the problem of cross-view image geo-localization.  ...  The street view images are from Google and the overhead 45 • bird's eye view images are from Bing.  ... 
arXiv:1703.07815v1 fatcat:fh7frtb4szgn5i4uyeyjwqaqgi
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