2026
Liu, Qingyang; Shatwell, David G.; Kulkarni, Parth Parag; Shah, Mubarak
Pixel-wise Geo-registration of Drone Images Conference
European Conference on Computer Vision (ECCV), Springer, 2026.
Abstract | Tags: ECCV, Geo-Localization, Remote Sensing | Links:
@conference{liu2026skyregb,
title = {Pixel-wise Geo-registration of Drone Images},
author = {Qingyang Liu and David G. Shatwell and Parth Parag Kulkarni and Mubarak Shah},
url = {https://qingyangliu.github.io/qingyang.github.io/static/Georegistration.pdf
https://ai.ucf.edu/wp-content/uploads/2026/09/Pixel-Wise-Geo-Registration-JPG.png
https://youtu.be/jgVuME3oFJ4?si=R-AkoBd3rlG_791Z},
year = {2026},
date = {2026-09-07},
urldate = {2026-09-07},
booktitle = {European Conference on Computer Vision (ECCV)},
publisher = {Springer},
abstract = {Cross-view geo-registration is the task of aligning a query image to a geodetically accurate reference (e.g., satellite image), so that each query pixel maps to a real-world GPS coordinate. Most existing work addresses the related problem of cross-view geo-localization, where the goal is typically to estimate the camera center using retrieval, classification, matching, or regression. Because these approaches do not provide dense supervision, they are poorly suited for learning and evaluating pixel-wise alignment. We introduce SkyReg, a geometry-aware geo-registration model that estimates the transformation between the query and reference images by explicitly modeling the 3D scene geometry. Applying this transformation to warp the query into the reference frame yields pixel-wise geo-localization, without relying on 2D point matches, and remains robust to occlusions and large viewpoint changes. To enable training and standardized evaluation, we release (i) SkyReg-Train, a large-scale dataset of drone–satellite images annotated with per-pixel GPS coordinates, depth maps, and camera parameters derived from LiDAR and structure-from-motion, and (ii) SkyReg-Bench, a held-out benchmark of unseen Urban and Suburban scenes with the same dense annotations. SkyReg achieves state-of-the-art performance against strong retrieval and homography baselines, demonstrating the value of geometry-aware models and dense geodetic benchmarks for cross-view geo-registration. Dataset available at https://parthpk.github.io/sk yreg-webpage.},
keywords = {ECCV, Geo-Localization, Remote Sensing},
pubstate = {published},
tppubtype = {conference}
}
2022
Zhu, Sijie; Shah, Mubarak; Chen, Chen
TransGeo: Transformer Is All You Need for Cross-view Image Geo-localization Conference
IEEE Computer Vision and Pattern Recognition, 2022.
Tags: Cross-View, CVPR, Geo-Localization, Transformers | Links:
@conference{nokey_107,
title = {TransGeo: Transformer Is All You Need for Cross-view Image Geo-localization},
author = {Sijie Zhu and Mubarak Shah and Chen Chen},
url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/11695.pdf
https://www.crcv.ucf.edu/wp-content/uploads/2018/11/11695-supp.pdf},
year = {2022},
date = {2022-06-19},
urldate = {2022-06-19},
publisher = {IEEE Computer Vision and Pattern Recognition},
keywords = {Cross-View, CVPR, Geo-Localization, Transformers},
pubstate = {published},
tppubtype = {conference}
}
Zhu, Sijie; Shah, Mubarak; Chen, Chen
TransGeo: Transformer Is All You Need for Cross-view Image Geo-localization Conference
IEEE Computer Vision and Pattern Recognition, 2022.
Tags: Cross-View, CVPR, Geo-Localization, Transformers | Links:
@conference{nokey_155,
title = {TransGeo: Transformer Is All You Need for Cross-view Image Geo-localization},
author = {Sijie Zhu and Mubarak Shah and Chen Chen},
url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/11695.pdf
https://www.crcv.ucf.edu/wp-content/uploads/2018/11/11695-supp.pdf},
year = {2022},
date = {2022-06-19},
urldate = {2022-06-19},
publisher = {IEEE Computer Vision and Pattern Recognition},
keywords = {Cross-View, CVPR, Geo-Localization, Transformers},
pubstate = {published},
tppubtype = {conference}
}
2021
Regmi, Krishna; Shah, Mubarak
Video Geo-Localization Employing Geo-Temporal Feature Learning and GPS Trajectory Smoothing Conference
International Conference on Computer Vision, 2021.
Abstract | Tags: Geo-Localization, ICCV, Transformers, Video Geo-localization | Links:
@conference{RegmiICCV2021,
title = {Video Geo-Localization Employing Geo-Temporal Feature Learning and GPS Trajectory Smoothing},
author = {Krishna Regmi and Mubarak Shah},
url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/video_geolocalization_ICCV2021.pdf
https://github.com/kregmi/VTE},
year = {2021},
date = {2021-10-11},
urldate = {2021-10-11},
booktitle = {International Conference on Computer Vision},
abstract = {In this paper, we address the problem of video geolocalization by proposing a Geo-Temporal Feature Learning (GTFL) Network to simultaneously learn the discriminative features for the query video frames and the gallery images for estimating the geo-spatial trajectory of a query video. Based on a transformer encoder architecture, our
GTFL model encodes query and gallery data separately, via two dedicated branches. The proposed GPS Loss and Clip Triplet Loss exploit the geographical and temporal proximity between the frames and the clips to jointly learn the query and the gallery features. We also propose a deep learning approach to trajectory smoothing by predicting the outliers in the estimated GPS positions and learning the offsets to smooth the trajectory. We build a large dataset from four different regions of USA; New York, San Francisco, Berkeley and Bay Area using BDD driving videos as query, and by collecting corresponding Google StreetView (GSV) Images for gallery. Extensive
evaluations of proposed method on this new dataset are provided. Code and dataset details is publicly available at
https://github.com/kregmi/VTE.},
keywords = {Geo-Localization, ICCV, Transformers, Video Geo-localization},
pubstate = {published},
tppubtype = {conference}
}
GTFL model encodes query and gallery data separately, via two dedicated branches. The proposed GPS Loss and Clip Triplet Loss exploit the geographical and temporal proximity between the frames and the clips to jointly learn the query and the gallery features. We also propose a deep learning approach to trajectory smoothing by predicting the outliers in the estimated GPS positions and learning the offsets to smooth the trajectory. We build a large dataset from four different regions of USA; New York, San Francisco, Berkeley and Bay Area using BDD driving videos as query, and by collecting corresponding Google StreetView (GSV) Images for gallery. Extensive
evaluations of proposed method on this new dataset are provided. Code and dataset details is publicly available at
https://github.com/kregmi/VTE.
Regmi, Krishna; Shah, Mubarak
Video Geo-Localization Employing Geo-Temporal Feature Learning and GPS Trajectory Smoothing Conference
International Conference on Computer Vision, 2021.
Abstract | Tags: Geo-Localization, ICCV, Transformers, Video Geo-localization | Links:
@conference{RegmiICCV2021b,
title = {Video Geo-Localization Employing Geo-Temporal Feature Learning and GPS Trajectory Smoothing},
author = {Krishna Regmi and Mubarak Shah},
url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/video_geolocalization_ICCV2021.pdf
https://github.com/kregmi/VTE},
year = {2021},
date = {2021-10-11},
urldate = {2021-10-11},
booktitle = {International Conference on Computer Vision},
abstract = {In this paper, we address the problem of video geolocalization by proposing a Geo-Temporal Feature Learning (GTFL) Network to simultaneously learn the discriminative features for the query video frames and the gallery images for estimating the geo-spatial trajectory of a query video. Based on a transformer encoder architecture, our
GTFL model encodes query and gallery data separately, via two dedicated branches. The proposed GPS Loss and Clip Triplet Loss exploit the geographical and temporal proximity between the frames and the clips to jointly learn the query and the gallery features. We also propose a deep learning approach to trajectory smoothing by predicting the outliers in the estimated GPS positions and learning the offsets to smooth the trajectory. We build a large dataset from four different regions of USA; New York, San Francisco, Berkeley and Bay Area using BDD driving videos as query, and by collecting corresponding Google StreetView (GSV) Images for gallery. Extensive
evaluations of proposed method on this new dataset are provided. Code and dataset details is publicly available at
https://github.com/kregmi/VTE.},
keywords = {Geo-Localization, ICCV, Transformers, Video Geo-localization},
pubstate = {published},
tppubtype = {conference}
}
GTFL model encodes query and gallery data separately, via two dedicated branches. The proposed GPS Loss and Clip Triplet Loss exploit the geographical and temporal proximity between the frames and the clips to jointly learn the query and the gallery features. We also propose a deep learning approach to trajectory smoothing by predicting the outliers in the estimated GPS positions and learning the offsets to smooth the trajectory. We build a large dataset from four different regions of USA; New York, San Francisco, Berkeley and Bay Area using BDD driving videos as query, and by collecting corresponding Google StreetView (GSV) Images for gallery. Extensive
evaluations of proposed method on this new dataset are provided. Code and dataset details is publicly available at
https://github.com/kregmi/VTE.
Regmi, Krishna
Exploring Relationships Between Ground and Aerial Views by Synthesis and Matching PhD Thesis
University of Central Florida, 2021.
Tags: Cross-View, GAN, Geo-Localization, Ph.D. Dissertation, Video Geo-localization | Links:
@phdthesis{Regmi2021,
title = {Exploring Relationships Between Ground and Aerial Views by Synthesis and Matching},
author = {Krishna Regmi},
url = {https://stars.library.ucf.edu/etd2020/747/},
year = {2021},
date = {2021-08-02},
urldate = {2021-08-02},
school = {University of Central Florida},
keywords = {Cross-View, GAN, Geo-Localization, Ph.D. Dissertation, Video Geo-localization},
pubstate = {published},
tppubtype = {phdthesis}
}
Regmi, Krishna
Exploring Relationships Between Ground and Aerial Views by Synthesis and Matching PhD Thesis
University of Central Florida, 2021.
Tags: Cross-View, GAN, Geo-Localization, Ph.D. Dissertation, Video Geo-localization | Links:
@phdthesis{Regmi2021b,
title = {Exploring Relationships Between Ground and Aerial Views by Synthesis and Matching},
author = {Krishna Regmi},
url = {https://stars.library.ucf.edu/etd2020/747/},
year = {2021},
date = {2021-08-02},
urldate = {2021-08-02},
school = {University of Central Florida},
keywords = {Cross-View, GAN, Geo-Localization, Ph.D. Dissertation, Video Geo-localization},
pubstate = {published},
tppubtype = {phdthesis}
}
2020
Gupta, Rohit; Shah, Mubarak
RescueNet: Joint Building Segmentation and Damage Assessment from Satellite Imagery Conference
International Conference on Pattern Recognition, 2020.
Tags: Geo-Localization, ICPR, Segmentation | Links:
@conference{Gupta2020,
title = {RescueNet: Joint Building Segmentation and Damage Assessment from Satellite Imagery},
author = {Rohit Gupta and Mubarak Shah},
url = {https://arxiv.org/pdf/2004.07312.pdf},
year = {2020},
date = {2020-04-15},
booktitle = {International Conference on Pattern Recognition},
keywords = {Geo-Localization, ICPR, Segmentation},
pubstate = {published},
tppubtype = {conference}
}
Gupta, Rohit; Shah, Mubarak
RescueNet: Joint Building Segmentation and Damage Assessment from Satellite Imagery Conference
International Conference on Pattern Recognition, 2020.
Tags: Geo-Localization, ICPR, Segmentation | Links:
@conference{Gupta2020b,
title = {RescueNet: Joint Building Segmentation and Damage Assessment from Satellite Imagery},
author = {Rohit Gupta and Mubarak Shah},
url = {https://arxiv.org/pdf/2004.07312.pdf},
year = {2020},
date = {2020-04-15},
booktitle = {International Conference on Pattern Recognition},
keywords = {Geo-Localization, ICPR, Segmentation},
pubstate = {published},
tppubtype = {conference}
}
2019
Regmi, Krishna; Shah, Mubarak
Bridging the Domain Gap for Ground-to-Aerial Image Matching Conference
International Conference on Computer Vision (ICCV 2019), Seoul, South Korea, Oct 27-Nov 2, 2019.
Tags: Cross-View Image Retrieval, Geo-Localization | Links:
@conference{Regmi2019,
title = {Bridging the Domain Gap for Ground-to-Aerial Image Matching},
author = {Krishna Regmi and Mubarak Shah},
url = {https://www.crcv.ucf.edu/wp-content/uploads/2020/02/Publications_Bridging-the-Domain-Gap-for-Ground-to-Aerial-Image-Matching_Final.pdf
https://www.crcv.ucf.edu/research/projects/bridging-the-domain-gap-for-ground-to-aerial-image-matching/},
year = {2019},
date = {2019-10-27},
publisher = {International Conference on Computer Vision (ICCV 2019), Seoul, South Korea, Oct 27-Nov 2},
keywords = {Cross-View Image Retrieval, Geo-Localization},
pubstate = {published},
tppubtype = {conference}
}
Regmi, Krishna; Shah, Mubarak
Bridging the Domain Gap for Ground-to-Aerial Image Matching Conference
International Conference on Computer Vision (ICCV 2019), Seoul, South Korea, Oct 27-Nov 2, 2019.
Tags: Cross-View Image Retrieval, Geo-Localization | Links:
@conference{Regmi2019b,
title = {Bridging the Domain Gap for Ground-to-Aerial Image Matching},
author = {Krishna Regmi and Mubarak Shah},
url = {https://www.crcv.ucf.edu/wp-content/uploads/2020/02/Publications_Bridging-the-Domain-Gap-for-Ground-to-Aerial-Image-Matching_Final.pdf
https://www.crcv.ucf.edu/research/projects/bridging-the-domain-gap-for-ground-to-aerial-image-matching/},
year = {2019},
date = {2019-10-27},
publisher = {International Conference on Computer Vision (ICCV 2019), Seoul, South Korea, Oct 27-Nov 2},
keywords = {Cross-View Image Retrieval, Geo-Localization},
pubstate = {published},
tppubtype = {conference}
}
2018
Regmi, Krishna; Borji, Ali
Cross-View Image Synthesis Using Conditional GANs Conference
IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018), Salt Lake City, UT, June 18-22, 2018, 2018.
Tags: Cross-View Image Synthesis, Geo-Localization | Links:
@conference{Regmi2018,
title = {Cross-View Image Synthesis Using Conditional GANs},
author = {Krishna Regmi and Ali Borji},
url = {https://www.crcv.ucf.edu/papers/cvpr2018/cross-view.pdf
https://www.crcv.ucf.edu/research/projects/cross-view-image-synthesis/},
year = {2018},
date = {2018-04-06},
urldate = {2018-04-06},
publisher = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018), Salt Lake City, UT, June 18-22, 2018},
keywords = {Cross-View Image Synthesis, Geo-Localization},
pubstate = {published},
tppubtype = {conference}
}
Regmi, Krishna; Borji, Ali
Cross-View Image Synthesis Using Conditional GANs Conference
IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018), Salt Lake City, UT, June 18-22, 2018, 2018.
Tags: Cross-View Image Synthesis, Geo-Localization | Links:
@conference{Regmi2018b,
title = {Cross-View Image Synthesis Using Conditional GANs},
author = {Krishna Regmi and Ali Borji},
url = {https://www.crcv.ucf.edu/papers/cvpr2018/cross-view.pdf
https://www.crcv.ucf.edu/research/projects/cross-view-image-synthesis/},
year = {2018},
date = {2018-04-06},
urldate = {2018-04-06},
publisher = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018), Salt Lake City, UT, June 18-22, 2018},
keywords = {Cross-View Image Synthesis, Geo-Localization},
pubstate = {published},
tppubtype = {conference}
}
Zemene, Eyasu; Tesfaye, Yonatan Tariku; Idrees, Haroon; Prati, Andrea; Pelillo, Marcello; Shah, Mubarak
Large-scale Image Geo-Localization Using Dominant Sets Journal Article
In: IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 41, pp. 148 - 161, 2018.
Tags: Clustering, Dominant Sets, Geo-Localization, Re-Ranking | Links:
@article{Mequanint2020,
title = {Large-scale Image Geo-Localization Using Dominant Sets},
author = {Eyasu Zemene and Yonatan Tariku Tesfaye and Haroon Idrees and Andrea Prati and Marcello Pelillo and Mubarak Shah},
url = {https://www.crcv.ucf.edu/wp-content/uploads/2020/05/Publications_Large-Scale-Image-Geo-Localization-Using-Dominant-Sets.pdf
https://www.crcv.ucf.edu/research/projects/large-scale-image-geo-localization-using-dominant-sets/},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {41},
pages = {148 - 161},
keywords = {Clustering, Dominant Sets, Geo-Localization, Re-Ranking},
pubstate = {published},
tppubtype = {article}
}
Zemene, Eyasu; Tesfaye, Yonatan Tariku; Idrees, Haroon; Prati, Andrea; Pelillo, Marcello; Shah, Mubarak
Large-scale Image Geo-Localization Using Dominant Sets Journal Article
In: IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 41, pp. 148 - 161, 2018.
Tags: Clustering, Dominant Sets, Geo-Localization, Re-Ranking | Links:
@article{Mequanint2020b,
title = {Large-scale Image Geo-Localization Using Dominant Sets},
author = {Eyasu Zemene and Yonatan Tariku Tesfaye and Haroon Idrees and Andrea Prati and Marcello Pelillo and Mubarak Shah},
url = {https://www.crcv.ucf.edu/wp-content/uploads/2020/05/Publications_Large-Scale-Image-Geo-Localization-Using-Dominant-Sets.pdf
https://www.crcv.ucf.edu/research/projects/large-scale-image-geo-localization-using-dominant-sets/},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {41},
pages = {148 - 161},
keywords = {Clustering, Dominant Sets, Geo-Localization, Re-Ranking},
pubstate = {published},
tppubtype = {article}
}
2015
Ardeshir, Shervin; Collins-Sibley, Kofi Malcolm; Shah, Mubarak
Geo-semantic Segmentation Conference
IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015), 2015.
Tags: Deep Learning, Geo-Localization, REU, Semantic Segmentation | Links:
@conference{Ardeshir2015,
title = {Geo-semantic Segmentation},
author = {Shervin Ardeshir and Kofi Malcolm Collins-Sibley and Mubarak Shah},
url = {https://www.crcv.ucf.edu/papers/cvpr2015/Geo-semantic_Segmentation.pdf
https://www.crcv.ucf.edu/projects/Geosemantic/ https://www.youtube.com/watch?v=jf5U3gdF5yEhttps://www.youtube.com/watch?v=jf5U3gdF5yE},
year = {2015},
date = {2015-01-29},
urldate = {2015-01-29},
publisher = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015)},
keywords = {Deep Learning, Geo-Localization, REU, Semantic Segmentation},
pubstate = {published},
tppubtype = {conference}
}
Ardeshir, Shervin; Collins-Sibley, Kofi Malcolm; Shah, Mubarak
Geo-semantic Segmentation Conference
IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015), 2015.
Tags: Deep Learning, Geo-Localization, REU, Semantic Segmentation | Links:
@conference{Ardeshir2015c,
title = {Geo-semantic Segmentation},
author = {Shervin Ardeshir and Kofi Malcolm Collins-Sibley and Mubarak Shah},
url = {https://www.crcv.ucf.edu/papers/cvpr2015/Geo-semantic_Segmentation.pdf
https://www.crcv.ucf.edu/projects/Geosemantic/ https://www.youtube.com/watch?v=jf5U3gdF5yEhttps://www.youtube.com/watch?v=jf5U3gdF5yE},
year = {2015},
date = {2015-01-29},
urldate = {2015-01-29},
publisher = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015)},
keywords = {Deep Learning, Geo-Localization, REU, Semantic Segmentation},
pubstate = {published},
tppubtype = {conference}
}
2014
Zamir, Amir Roshan; Shah, Mubarak
Image Geo-localization Based on Multiple Nearest Neighbor Feature Matching Using Generalized Graphs Journal Article
In: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014.
Tags: Deep Learning, Geo-Localization, Video Geo-localization | Links:
@article{Zamir2014b,
title = {Image Geo-localization Based on Multiple Nearest Neighbor Feature Matching Using Generalized Graphs},
author = {Amir Roshan Zamir and Mubarak Shah},
url = {https://www.crcv.ucf.edu/papers/PAMI_Amir%20Zamir.pdf},
year = {2014},
date = {2014-01-29},
urldate = {2014-01-29},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {Deep Learning, Geo-Localization, Video Geo-localization},
pubstate = {published},
tppubtype = {article}
}
Zamir, Amir Roshan; Shah, Mubarak
Image Geo-localization Based on Multiple Nearest Neighbor Feature Matching Using Generalized Graphs Journal Article
In: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014.
Tags: Deep Learning, Geo-Localization, Video Geo-localization | Links:
@article{Zamir2014bb,
title = {Image Geo-localization Based on Multiple Nearest Neighbor Feature Matching Using Generalized Graphs},
author = {Amir Roshan Zamir and Mubarak Shah},
url = {https://www.crcv.ucf.edu/papers/PAMI_Amir%20Zamir.pdf},
year = {2014},
date = {2014-01-29},
urldate = {2014-01-29},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {Deep Learning, Geo-Localization, Video Geo-localization},
pubstate = {published},
tppubtype = {article}
}
Zamir, Amir Roshan; Ardeshir, Shervin; Shah, Mubarak
GPS-Tag Refinement using Random Walks with an Adaptive Damping Factor Conference
CVPR 2014, 2014.
Tags: Deep Learning, Geo-Localization | Links:
@conference{Zamir2014,
title = {GPS-Tag Refinement using Random Walks with an Adaptive Damping Factor},
author = {Amir Roshan Zamir and Shervin Ardeshir and Mubarak Shah},
url = {https://www.crcv.ucf.edu/papers/cvpr2014/TagRefinement_CVPR14_Camready_v5.pdf
https://www.crcv.ucf.edu/papers/cvpr2014/video-spotlight-514.mp4 http://www.youtube.com/watch?v=0qbKQHJu-ow&list=PLd3hlSJsX_InWZEJNJG_yoGs-zy-9D-J7&feature=share},
year = {2014},
date = {2014-01-26},
urldate = {2014-01-26},
publisher = {CVPR 2014},
keywords = {Deep Learning, Geo-Localization},
pubstate = {published},
tppubtype = {conference}
}
Zamir, Amir Roshan; Ardeshir, Shervin; Shah, Mubarak
GPS-Tag Refinement using Random Walks with an Adaptive Damping Factor Conference
CVPR 2014, 2014.
Tags: Deep Learning, Geo-Localization | Links:
@conference{Zamir2014e,
title = {GPS-Tag Refinement using Random Walks with an Adaptive Damping Factor},
author = {Amir Roshan Zamir and Shervin Ardeshir and Mubarak Shah},
url = {https://www.crcv.ucf.edu/papers/cvpr2014/TagRefinement_CVPR14_Camready_v5.pdf
https://www.crcv.ucf.edu/papers/cvpr2014/video-spotlight-514.mp4 http://www.youtube.com/watch?v=0qbKQHJu-ow&list=PLd3hlSJsX_InWZEJNJG_yoGs-zy-9D-J7&feature=share},
year = {2014},
date = {2014-01-26},
urldate = {2014-01-26},
publisher = {CVPR 2014},
keywords = {Deep Learning, Geo-Localization},
pubstate = {published},
tppubtype = {conference}
}
Ardeshir, Shervin; Zamir, Amir Roshan; Torroella, Alejandro; Shah, Mubarak
GIS-Assisted Object Detection and Geospatial Localization Conference
ECCV 2014, 2014.
Tags: Deep Learning, Geo-Localization, GIS, Object Detection, REU | Links:
@conference{Ardeshir2015b,
title = {GIS-Assisted Object Detection and Geospatial Localization},
author = {Shervin Ardeshir and Amir Roshan Zamir and Alejandro Torroella and Mubarak Shah},
url = {https://www.crcv.ucf.edu/papers/GIS_ECCV14.pdf},
year = {2014},
date = {2014-01-25},
urldate = {2014-01-25},
publisher = {ECCV 2014},
keywords = {Deep Learning, Geo-Localization, GIS, Object Detection, REU},
pubstate = {published},
tppubtype = {conference}
}
Ardeshir, Shervin; Zamir, Amir Roshan; Torroella, Alejandro; Shah, Mubarak
GIS-Assisted Object Detection and Geospatial Localization Conference
ECCV 2014, 2014.
Tags: Deep Learning, Geo-Localization, GIS, Object Detection, REU | Links:
@conference{Ardeshir2015bb,
title = {GIS-Assisted Object Detection and Geospatial Localization},
author = {Shervin Ardeshir and Amir Roshan Zamir and Alejandro Torroella and Mubarak Shah},
url = {https://www.crcv.ucf.edu/papers/GIS_ECCV14.pdf},
year = {2014},
date = {2014-01-25},
urldate = {2014-01-25},
publisher = {ECCV 2014},
keywords = {Deep Learning, Geo-Localization, GIS, Object Detection, REU},
pubstate = {published},
tppubtype = {conference}
}
Zamir, Amir Roshan
Visual Geo-Localization and Location-Aware Image Understanding PhD Thesis
University of Central Florida, 2014.
Tags: Deep Learning, Geo-Localization, GMCP, Video Geo-localization | Links:
@phdthesis{Zamir2014c,
title = {Visual Geo-Localization and Location-Aware Image Understanding},
author = {Amir Roshan Zamir},
url = {https://www.crcv.ucf.edu/papers/theses/Thesis_Amir_Zamir.pdf},
year = {2014},
date = {2014-01-21},
urldate = {2014-01-21},
school = {University of Central Florida},
keywords = {Deep Learning, Geo-Localization, GMCP, Video Geo-localization},
pubstate = {published},
tppubtype = {phdthesis}
}
Zamir, Amir Roshan
Visual Geo-Localization and Location-Aware Image Understanding PhD Thesis
University of Central Florida, 2014.
Tags: Deep Learning, Geo-Localization, GMCP, Video Geo-localization | Links:
@phdthesis{Zamir2014cb,
title = {Visual Geo-Localization and Location-Aware Image Understanding},
author = {Amir Roshan Zamir},
url = {https://www.crcv.ucf.edu/papers/theses/Thesis_Amir_Zamir.pdf},
year = {2014},
date = {2014-01-21},
urldate = {2014-01-21},
school = {University of Central Florida},
keywords = {Deep Learning, Geo-Localization, GMCP, Video Geo-localization},
pubstate = {published},
tppubtype = {phdthesis}
}
2013
Zamir, Amir Roshan; Dehghan, Afshin; Shah, Mubarak
Visual Business Recognition - A Multimodal Approach Conference
ACM Multimedia 2013, 2013.
Tags: Deep Learning, Geo-Localization, Video Geo-localization | Links:
@conference{Zamir2013,
title = {Visual Business Recognition - A Multimodal Approach},
author = {Amir Roshan Zamir and Afshin Dehghan and Mubarak Shah},
url = {https://www.crcv.ucf.edu/papers/ACM13_BizRec.pdf
http://youtu.be/EIr2Dkj-T-o
https://www.crcv.ucf.edu/projects/Business-Recognition/},
year = {2013},
date = {2013-01-16},
urldate = {2013-01-16},
publisher = {ACM Multimedia 2013},
keywords = {Deep Learning, Geo-Localization, Video Geo-localization},
pubstate = {published},
tppubtype = {conference}
}
Zamir, Amir Roshan; Dehghan, Afshin; Shah, Mubarak
Visual Business Recognition - A Multimodal Approach Conference
ACM Multimedia 2013, 2013.
Tags: Deep Learning, Geo-Localization, Video Geo-localization | Links:
@conference{Zamir2013b,
title = {Visual Business Recognition - A Multimodal Approach},
author = {Amir Roshan Zamir and Afshin Dehghan and Mubarak Shah},
url = {https://www.crcv.ucf.edu/papers/ACM13_BizRec.pdf
http://youtu.be/EIr2Dkj-T-o
https://www.crcv.ucf.edu/projects/Business-Recognition/},
year = {2013},
date = {2013-01-16},
urldate = {2013-01-16},
publisher = {ACM Multimedia 2013},
keywords = {Deep Learning, Geo-Localization, Video Geo-localization},
pubstate = {published},
tppubtype = {conference}
}
2012
Vaca-Castano, Gonzalo; Zamir, Amir Roshan; Shah, Mubarak
City Scale Geo-Spatial Trajectory Estimation of a Moving Camera Conference
Computer Vision and Pattern Recognition 2012, 2012.
Tags: Deep Learning, Geo-Localization, Video Geo-localization | Links:
@conference{Vaca-Castano2012,
title = {City Scale Geo-Spatial Trajectory Estimation of a Moving Camera},
author = {Gonzalo Vaca-Castano and Amir Roshan Zamir and Mubarak Shah},
url = {https://www.crcv.ucf.edu/papers/cvpr2012/CVPR12_Amir.pdf},
year = {2012},
date = {2012-12-24},
urldate = {2012-12-24},
publisher = {Computer Vision and Pattern Recognition 2012},
keywords = {Deep Learning, Geo-Localization, Video Geo-localization},
pubstate = {published},
tppubtype = {conference}
}
Vaca-Castano, Gonzalo; Zamir, Amir Roshan; Shah, Mubarak
City Scale Geo-Spatial Trajectory Estimation of a Moving Camera Conference
Computer Vision and Pattern Recognition 2012, 2012.
Tags: Deep Learning, Geo-Localization, Video Geo-localization | Links:
@conference{Vaca-Castano2012b,
title = {City Scale Geo-Spatial Trajectory Estimation of a Moving Camera},
author = {Gonzalo Vaca-Castano and Amir Roshan Zamir and Mubarak Shah},
url = {https://www.crcv.ucf.edu/papers/cvpr2012/CVPR12_Amir.pdf},
year = {2012},
date = {2012-12-24},
urldate = {2012-12-24},
publisher = {Computer Vision and Pattern Recognition 2012},
keywords = {Deep Learning, Geo-Localization, Video Geo-localization},
pubstate = {published},
tppubtype = {conference}
}
2011
Zamir, Amir Roshan; Darino, Alexander; Patrick, Ryan; Shah, Mubarak
Street View Challenge: Identification of Commercial Entities in Street View Imagery Conference
ICMLA, 2011.
Tags: Geo-Localization, REU
@conference{Zamir2011,
title = {Street View Challenge: Identification of Commercial Entities in Street View Imagery},
author = {Amir Roshan Zamir and Alexander Darino and Ryan Patrick and Mubarak Shah},
year = {2011},
date = {2011-12-18},
urldate = {2011-12-18},
booktitle = {ICMLA},
keywords = {Geo-Localization, REU},
pubstate = {published},
tppubtype = {conference}
}
Zamir, Amir Roshan; Darino, Alexander; Patrick, Ryan; Shah, Mubarak
Street View Challenge: Identification of Commercial Entities in Street View Imagery Conference
ICMLA, 2011.
Tags: Geo-Localization, REU
@conference{Zamir2011b,
title = {Street View Challenge: Identification of Commercial Entities in Street View Imagery},
author = {Amir Roshan Zamir and Alexander Darino and Ryan Patrick and Mubarak Shah},
year = {2011},
date = {2011-12-18},
urldate = {2011-12-18},
booktitle = {ICMLA},
keywords = {Geo-Localization, REU},
pubstate = {published},
tppubtype = {conference}
}
2010
Zamir, Amir Roshan; Shah, Mubarak
Accurate Image Localization Based on Google Maps Street View Conference
European Conference on Computer Vision (ECCV), 2010.
Tags: Deep Learning, Geo-Localization | Links:
@conference{Zamir2010,
title = {Accurate Image Localization Based on Google Maps Street View},
author = {Amir Roshan Zamir and Mubarak Shah},
url = {https://www.crcv.ucf.edu/papers/eccv2010/Zamir_ECCV_2010.pdf},
year = {2010},
date = {2010-12-12},
urldate = {2010-12-12},
publisher = {European Conference on Computer Vision (ECCV)},
keywords = {Deep Learning, Geo-Localization},
pubstate = {published},
tppubtype = {conference}
}
Zamir, Amir Roshan; Shah, Mubarak
Accurate Image Localization Based on Google Maps Street View Conference
European Conference on Computer Vision (ECCV), 2010.
Tags: Deep Learning, Geo-Localization | Links:
@conference{Zamir2010b,
title = {Accurate Image Localization Based on Google Maps Street View},
author = {Amir Roshan Zamir and Mubarak Shah},
url = {https://www.crcv.ucf.edu/papers/eccv2010/Zamir_ECCV_2010.pdf},
year = {2010},
date = {2010-12-12},
urldate = {2010-12-12},
publisher = {European Conference on Computer Vision (ECCV)},
keywords = {Deep Learning, Geo-Localization},
pubstate = {published},
tppubtype = {conference}
}