Faculty Research Award (Crossview Convolutional Networks)

This project seeks to develop deep convolutional neural networks that can combine ground-level and overhead imagery for remote sensing tasks. We are exploring several approaches. See below for work that was supported by this award.

Related Publication(s)

  1. Thumbnail for Learning a Dynamic Map of Visual Appearance
    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.
  2. Thumbnail for Dynamic Traffic Modeling from Overhead Imagery
    Workman S, Jacobs N. 2020. Dynamic Traffic Modeling from Overhead Imagery. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). DOI: 10.1109/CVPR42600.2020.01233.
  3. Thumbnail for Implicit Land Use Mapping Using Social Media Imagery
    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.
  4. Thumbnail for A Unified Model for Near and Remote Sensing
    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.
  5. Thumbnail for Understanding and Mapping Natural Beauty
    Workman S, Souvenir R, Jacobs N. 2017. Understanding and Mapping Natural Beauty. In: IEEE International Conference on Computer Vision (ICCV). DOI: 10.1109/ICCV.2017.596.
  6. Thumbnail for Predicting Ground-Level Scene Layout from Aerial Imagery
    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.
  7. Thumbnail for Crossview Convolutional Networks
    Jacobs N, Workman S, Zhai M. 2016. Crossview Convolutional Networks. In: IEEE Applied Imagery and Pattern Recognition (AIPR). DOI: 10.1109/AIPR.2016.8010593.

Acknowledgements

This work was supported by a Google Faculty Research Award.

Grant Details

  • Role: PI
  • Funding Source: Google
  • Budget: $46,209
  • Duration: 2016 – 2017