Dataset Description

A large dataset containing millions of pairs of ground-level and aerial/satellite images from across the United States.

Dataset Download

If you want access to the CVUSA dataset, please submit a request. This will grant you access to both the original CVUSA dataset and the smaller subset created for the Zhai et al. (CVPR 2017) paper.

Dataset Citation

If you use the dataset, please cite the following publications:

1. Workman S, Souvenir R, Jacobs N. 2015. Wide-Area Image Geolocalization with Aerial Reference Imagery. In: IEEE International Conference on Computer Vision (ICCV). 1–9. DOI: 10.1109/ICCV.2015.451.
   bibtex | paper | website | doi | code

   close  

   @inproceedings{workman2015localize,
     annotation = {spotlight,ugrad,dataset_cvusa,waldo,nvidia,remote_sensing,social_media,localization},
     author = {Workman, Scott and Souvenir, Richard and Jacobs, Nathan},
     booktitle = {IEEE International Conference on Computer Vision (ICCV)},
     code = {https://github.com/scottworkman/deeplyfound},
     pages = {1--9},
     pdf = {http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Workman_Wide-Area_Image_Geolocalization_ICCV_2015_paper.pdf},
     project = {http://cs.uky.edu/%7escott/research/deeplyfound/},
     thumbnail = {/thumbnails/crossview.jpg},
     title = {Wide-Area Image Geolocalization with Aerial Reference Imagery},
     doi = {10.1109/ICCV.2015.451},
     note = {Acceptance rate: 30.3\%},
     year = {2015}
   }
   

Related Publication(s)

1. Salem T, Workman S, Jacobs N. 2020. Learning a Dynamic Map of Visual Appearance. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). DOI: 10.1109/CVPR42600.2020.01245.
   bibtex | paper | website | doi | tweet

   close  

   @inproceedings{salem2020dynamic,
     author = {Salem, Tawfiq and Workman, Scott and Jacobs, Nathan},
     booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
     title = {Learning a Dynamic Map of Visual Appearance},
     doi = {10.1109/CVPR42600.2020.01245},
     year = {2020},
     annotation = {google_crossview,skyfinder,localization,career,remote_sensing,spotlight,timelapse,cvusa,social_media},
     date-modified = {2020-03-31 16:54:47 -0400},
     project = {https://tsalem.github.io/DynamicMaps/},
     note = {Acceptance rate: 25\%},
     pdf = {http://openaccess.thecvf.com/content_CVPR_2020/papers/Salem_Learning_a_Dynamic_Map_of_Visual_Appearance_CVPR_2020_paper.pdf},
     video = {https://www.youtube.com/watch?v=OVTvgWLlJmM},
     tweet = {https://twitter.com/jacobsn/status/1272637141797961729},
     thumbnail = {/thumbnails/dynamic_visual.jpg}
   }
   

2. Greenwell C, Workman S, Jacobs N. 2019. Implicit Land Use Mapping Using Social Media Imagery. In: IEEE Applied Imagery and Pattern Recognition (AIPR). DOI: 10.1109/AIPR47015.2019.9174570.
   bibtex | paper | doi

   close  

   @inproceedings{greenwell2019implicit,
     annotation = {abstract,google_crossview,amazon,nvidia,remote_sensing,social_media,cvusa},
     author = {Greenwell, Connor and Workman, Scott and Jacobs, Nathan},
     booktitle = {IEEE Applied Imagery and Pattern Recognition (AIPR)},
     title = {Implicit Land Use Mapping Using Social Media Imagery},
     note = {(oral)},
     doi = {10.1109/AIPR47015.2019.9174570},
     pdf = {https://drive.google.com/open?id=1XAmhngKoXOKfAqUwRBCOhCWSBLQ84mqR},
     year = {2019}
   }
   

3. Salem T, Greenwell C, Blanton H, Jacobs N. 2019. Learning to Map Nearly Anything. In: IEEE International Geoscience and Remote Sensing Symposium (IGARSS). DOI: 10.1109/IGARSS.2019.8900646.
   bibtex | paper | doi

   close  

   @inproceedings{salem2019anything,
     annotation = {career,remote_sensing,social_media,cvusa},
     author = {Salem, Tawfiq and Greenwell, Connor and Blanton, Hunter and Jacobs, Nathan},
     booktitle = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
     title = {Learning to Map Nearly Anything},
     note = {(oral)},
     pdf = {https://arxiv.org/pdf/1909.06928},
     doi = {10.1109/IGARSS.2019.8900646},
     year = {2019}
   }
   

4. Greenwell C, Workman S, Jacobs N. 2018. What Goes Where: Predicting Object Distributions from Above. In: IEEE International Geoscience and Remote Sensing Symposium (IGARSS). DOI: 10.1109/IGARSS.2018.8519251.
   bibtex | paper | website | doi

   close  

   @inproceedings{greenwell2018objects,
     annotation = {career,remote_sensing,social_media,cvusa},
     author = {Greenwell, Connor and Workman, Scott and Jacobs, Nathan},
     booktitle = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
     project = {https://connorgreenwell.github.io/publication/what_goes_where/},
     title = {What Goes Where: Predicting Object Distributions from Above},
     pdf = {https://arxiv.org/pdf/1808.00995},
     doi = {10.1109/IGARSS.2018.8519251},
     year = {2018}
   }
   

5. Zhai M, Bessinger Z, Workman S, Jacobs N. 2017. Predicting Ground-Level Scene Layout from Aerial Imagery. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). DOI: 10.1109/CVPR.2017.440.
   bibtex | paper | doi | tweet | code

   close  

   @inproceedings{zhai2017layout,
     annotation = {google_crossview,synthesis,localization,spotlight,nvidia,amazon,career,remote_sensing,cvusa},
     author = {Zhai, Menghua and Bessinger, Zach and Workman, Scott and Jacobs, Nathan},
     booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
     code = {https://github.com/viibridges/crossnet},
     doi = {10.1109/CVPR.2017.440},
     note = {Acceptance rate: 29.2\%},
     pdf = {https://openaccess.thecvf.com/content_cvpr_2017/papers/Zhai_Predicting_Ground-Level_Scene_CVPR_2017_paper.pdf},
     tweet = {https://twitter.com/jacobsn/status/837012306814980097},
     thumbnail = {/thumbnails/predict_layout.jpg},
     title = {Predicting Ground-Level Scene Layout from Aerial Imagery},
     year = {2017}
   }
   

6. Workman S, Souvenir R, Jacobs N. 2015. Wide-Area Image Geolocalization with Aerial Reference Imagery. In: IEEE International Conference on Computer Vision (ICCV). 1–9. DOI: 10.1109/ICCV.2015.451.
   bibtex | paper | website | doi | code

   close  

   @inproceedings{workman2015localize,
     annotation = {spotlight,ugrad,dataset_cvusa,waldo,nvidia,remote_sensing,social_media,localization},
     author = {Workman, Scott and Souvenir, Richard and Jacobs, Nathan},
     booktitle = {IEEE International Conference on Computer Vision (ICCV)},
     code = {https://github.com/scottworkman/deeplyfound},
     pages = {1--9},
     pdf = {http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Workman_Wide-Area_Image_Geolocalization_ICCV_2015_paper.pdf},
     project = {http://cs.uky.edu/%7escott/research/deeplyfound/},
     thumbnail = {/thumbnails/crossview.jpg},
     title = {Wide-Area Image Geolocalization with Aerial Reference Imagery},
     doi = {10.1109/ICCV.2015.451},
     note = {Acceptance rate: 30.3\%},
     year = {2015}
   }
   

7. Workman S, Jacobs N. 2015. On the Location Dependence of Convolutional Neural Network Features. In: IEEE/ISPRS Workshop: Large Scale Computer Vision for Remote Sensing (EARTHVISION). 1–9. DOI: 10.1109/CVPRW.2015.7301385.
   bibtex | paper | doi

   close  

   @inproceedings{workman2015geocnn,
     annotation = {waldo,localization,calibration,cvusa,social_media,remote_sensing},
     author = {Workman, Scott and Jacobs, Nathan},
     booktitle = {IEEE/ISPRS Workshop: Large Scale Computer Vision for Remote Sensing (EARTHVISION)},
     note = {Acceptance rate: 30\%},
     pages = {1--9},
     pdf = {http://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W13/papers/Workman_On_the_Location_2015_CVPR_paper.pdf},
     title = {On the Location Dependence of Convolutional Neural Network Features},
     doi = {10.1109/CVPRW.2015.7301385},
     year = {2015}
   }