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 Brooklyn and Queens (Near/Remote) dataset, please submit a request. This will grant you access to both the original dataset used in the 2017 paper and the extension for the 2022 paper.

Dataset Citation

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

1. Workman S, Rafique MU, Blanton H, Jacobs N. 2022. Revisiting Near/Remote Sensing with Geospatial Attention. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). DOI: 10.1109/CVPR52688.2022.00182.
   bibtex | paper | doi | tweet

   close  

   @inproceedings{workman2022nearremote,
     annotation = {spotlight,remote_sensing,social_media,near_remote,dataset_brooklyn_and_queens},
     author = {Workman, Scott and Rafique, Muhammad Usman and Blanton, Hunter and Jacobs, Nathan},
     booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
     thumbnail = {/thumbnails/nearremote2.jpg},
     title = {Revisiting Near/Remote Sensing with Geospatial Attention},
     pdf = {https://arxiv.org/pdf/2204.01807},
     doi = {10.1109/CVPR52688.2022.00182},
     tweet = {https://twitter.com/multimodal_lab/status/1511779656684343299},
     note = {Acceptance rate: 25.33\%},
     month = jun,
     year = {2022}
   }
   

2. Workman S, Zhai M, Crandall D, Jacobs N. 2017. A Unified Model for Near and Remote Sensing. In: IEEE International Conference on Computer Vision (ICCV). DOI: 10.1109/ICCV.2017.293.
   bibtex | paper | website | doi

   close  

   @inproceedings{workman2017nearremote,
     annotation = {google_crossview,career,spotlight,remote_sensing,social_media,near_remote,dataset_brooklyn_and_queens},
     author = {Workman, Scott and Zhai, Menghua and Crandall, David and Jacobs, Nathan},
     booktitle = {IEEE International Conference on Computer Vision (ICCV)},
     project = {http://cs.uky.edu/%7escott/research/unified/},
     pdf = {https://arxiv.org/pdf/1708.03035},
     thumbnail = {/thumbnails/nearremote.jpg},
     title = {A Unified Model for Near and Remote Sensing},
     note = {Acceptance rate: 28.9\%},
     doi = {10.1109/ICCV.2017.293},
     year = {2017}
   }
   

Related Publication(s)

1. Workman S, Rafique MU, Blanton H, Jacobs N. 2022. Revisiting Near/Remote Sensing with Geospatial Attention. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). DOI: 10.1109/CVPR52688.2022.00182.
   bibtex | paper | doi | tweet

   close  

   @inproceedings{workman2022nearremote,
     annotation = {spotlight,remote_sensing,social_media,near_remote,dataset_brooklyn_and_queens},
     author = {Workman, Scott and Rafique, Muhammad Usman and Blanton, Hunter and Jacobs, Nathan},
     booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
     thumbnail = {/thumbnails/nearremote2.jpg},
     title = {Revisiting Near/Remote Sensing with Geospatial Attention},
     pdf = {https://arxiv.org/pdf/2204.01807},
     doi = {10.1109/CVPR52688.2022.00182},
     tweet = {https://twitter.com/multimodal_lab/status/1511779656684343299},
     note = {Acceptance rate: 25.33\%},
     month = jun,
     year = {2022}
   }
   

2. Workman S, Zhai M, Crandall D, Jacobs N. 2017. A Unified Model for Near and Remote Sensing. In: IEEE International Conference on Computer Vision (ICCV). DOI: 10.1109/ICCV.2017.293.
   bibtex | paper | website | doi

   close  

   @inproceedings{workman2017nearremote,
     annotation = {google_crossview,career,spotlight,remote_sensing,social_media,near_remote,dataset_brooklyn_and_queens},
     author = {Workman, Scott and Zhai, Menghua and Crandall, David and Jacobs, Nathan},
     booktitle = {IEEE International Conference on Computer Vision (ICCV)},
     project = {http://cs.uky.edu/%7escott/research/unified/},
     pdf = {https://arxiv.org/pdf/1708.03035},
     thumbnail = {/thumbnails/nearremote.jpg},
     title = {A Unified Model for Near and Remote Sensing},
     note = {Acceptance rate: 28.9\%},
     doi = {10.1109/ICCV.2017.293},
     year = {2017}
   }