• Role: PI
• Funding Source: NSF
• Duration: 2016 – 2021

Overview

Billions of geotagged and time-stamped images are publicly available via the Internet, providing a rich record of the appearance of people, places, and things across the globe. These images are a largely untapped resource that could be used to improve our understanding of the world and how it changes over time. This project develops automated methods of extracting useful information from this imagery and fusing it into high-resolution global models that capture geo-temporal trends. Once the trends have been captured, these models are used to improve performance on computer vision tasks and make geotagged imagery a usable and navigable resource for education and research in other disciplines. The project includes an education and outreach component that brings real-world problems to computer science (CS) students, mentors students across the educational spectrum, and makes the research accessible to the public.

This project develops computer vision technologies to capture spatial and temporal appearance trends and is organized into four main research thrusts: (1) investigating novel methods for extracting information from Internet imagery using weakly supervised learning, (2) developing techniques that integrate ground-level imagery with aerial and satellite data to model the expected image appearance anywhere in the world at any time, (3) evaluating methods for using such models to improve the performance of computer vision algorithms, and (4) automatically creating visual representations that make it possible for novice users to explore the learned geo-temporal trends via the Internet.

See the NSF Award Announcement for additional details.

Related Publication(s)

1. Padilha R, Salem T, Workman S, Andaló FA, Rocha A, Jacobs N. 2022. Content-Based Detection of Temporal Metadata Manipulation. IEEE Transactions on Information Forensics and Security:1316–1327. DOI: 10.1109/TIFS.2022.3159154.
   bibtex | paper | website | doi | tweet | code

   close  

   @article{padilha2022contentbased,
     title = {Content-Based Detection of Temporal Metadata Manipulation},
     author = {Padilha, Rafael and Salem, Tawfiq and Workman, Scott and Andaló, Fernanda A. and Rocha, Anderson and Jacobs, Nathan},
     year = {2022},
     month = mar,
     day = {11},
     thumbnail = {/thumbnails/tampering.jpg},
     doi = {10.1109/TIFS.2022.3159154},
     journal = {IEEE Transactions on Information Forensics and Security},
     author+an = {6=highlight},
     pdf = {https://arxiv.org/pdf/2103.04736},
     pages = {1316--1327},
     project = {https://rafaspadilha.github.io/publication/padilha22content/},
     code = {https://github.com/rafaspadilha/timestampVerificationTIFS},
     tweet = {https://twitter.com/jacobsn/status/1498729825523015681},
     annotation = {spotlight,webcam,calibration,skyfinder,localization,career,social_media,remote_sensing}
   }
   

2. Blanton H, Workman S, Jacobs N. 2022. A Structure-Aware Method for Direct Pose Estimation. In: IEEE Winter Conference on Applications of Computer Vision (WACV). DOI: 10.1109/WACV51458.2022.00028.
   bibtex | paper | doi

   close  

   @inproceedings{blanton2022structureaware,
     title = {A Structure-Aware Method for Direct Pose Estimation},
     author = {Blanton, Hunter and Workman, Scott and Jacobs, Nathan},
     year = {2022},
     doi = {10.1109/WACV51458.2022.00028},
     month = jan,
     booktitle = {IEEE Winter Conference on Applications of Computer Vision (WACV)},
     author+an = {3=highlight},
     pdf = {https://arxiv.org/pdf/2012.12360},
     annotation = {calibration,localization,career,spotlight},
     thumbnail = {/thumbnails/direct_pose.jpg},
     primaryclass = {cs.CV}
   }
   

3. Rafique MU, Zhu J, Jacobs N. 2021. Automatic Segmentation of Sinkholes Using a Convolutional Neural Network. Earth and Space Science:19. DOI: 10.1002/essoar.10509794.1.
   bibtex | paper | doi

   close  

   @article{rafique2021sinkhole,
     annotation = {career,remote_sensing,lidar,3d},
     author = {Rafique, Muhammad Usman and Zhu, Junfeng and Jacobs, Nathan},
     title = {Automatic Segmentation of Sinkholes Using a Convolutional Neural Network},
     journal = {Earth and Space Science},
     pages = {19},
     year = {2021},
     month = dec,
     day = {22},
     doi = {10.1002/essoar.10509794.1},
     pdf = {https://www.essoar.org/doi/abs/10.1002/essoar.10509794.1},
     note = {Impact factor: 3.138}
   }
   

4. Liang G, Su Y, Lin S-C, Zhang Y, Zhang Y, Jacobs N. 2020. Optical Wavelength Guided Self-Supervised Feature Learning For Galaxy Cluster Richness Estimate. In: Workshop on Machine Learning and the Physical Sciences at the 34th Conference on Neural Information Processing Systems.
   bibtex | paper

   close  

   @inproceedings{liang2020optical,
     annotation = {career,astrophysics},
     author = {Liang, Gongbo and Su, Yuanyuan and Lin, Sheng-Chieh and Zhang, Yu and Zhang, Yuanyuan and Jacobs, Nathan},
     author+an = {6=highlight},
     booktitle = {Workshop on Machine Learning and the Physical Sciences at the 34th Conference on Neural Information Processing Systems},
     title = {Optical Wavelength Guided Self-Supervised Feature Learning For Galaxy Cluster Richness Estimate},
     year = {2020},
     pdf = {https://arxiv.org/pdf/2012.02368},
     month = dec
   }
   

5. Zhu J, Nolte A, Jacobs N, Ye M. 2020. Machine Learning in Identifying Karst Sinkholes from LiDAR-Derived Topographic Depressions in the Bluegrass Region of Kentucky. Journal of Hydrology. DOI: 10.1016/j.jhydrol.2020.125049.
   bibtex | doi

   close  

   @article{zhu2020sinkhole,
     annotation = {career,remote_sensing,lidar,3d,pointcloud},
     author = {Zhu, Junfeng and Nolte, Adam and Jacobs, Nathan and Ye, Ming},
     journal = {Journal of Hydrology},
     title = {Machine Learning in Identifying Karst Sinkholes from {LiDAR}-Derived Topographic Depressions in the {Bluegrass} Region of {Kentucky}},
     year = {2020},
     month = sep,
     doi = {10.1016/j.jhydrol.2020.125049},
     note = {Impact factor: 4.405}
   }
   

6. Liang G, Zhang Y, Wang X, Jacobs N. 2020. Improved Trainable Calibration Method for Neural Networks. In: British Machine Vision Conference (BMVC).
   bibtex | paper | website | tweet

   close  

   @inproceedings{liang2020bmvc,
     annotation = {spotlight,career,medical},
     author = {Liang, Gongbo and Zhang, Yu and Wang, Xiaoqing and Jacobs, Nathan},
     booktitle = {British Machine Vision Conference (BMVC)},
     title = {Improved Trainable Calibration Method for Neural Networks},
     thumbnail = {/thumbnails/dca.jpg},
     pdf = {https://www.bmvc2020-conference.com/assets/papers/0059.pdf},
     tweet = {https://twitter.com/jacobsn/status/1296147802297442312},
     project = {http://www.gb-liang.com/dca.html},
     month = sep,
     year = {2020}
   }
   

7. Rafique MU, Blanton H, Snavely N, Jacobs N. 2020. Generative Appearance Flow: A Hybrid Approach for Outdoor View Synthesis. In: British Machine Vision Conference (BMVC).
   bibtex | paper | website | tweet | code

   close  

   @inproceedings{rafique2020bmvc,
     annotation = {career,spotlight},
     author = {Rafique, M. Usman and Blanton, Hunter and Snavely, Noah and Jacobs, Nathan},
     booktitle = {British Machine Vision Conference (BMVC)},
     tweet = {https://twitter.com/jacobsn/status/1295489569576681472},
     title = {{Generative Appearance Flow}: A Hybrid Approach for Outdoor View Synthesis},
     year = {2020},
     thumbnail = {/thumbnails/gaf.jpg},
     code = {https://github.com/Usman-Rafique/gaf},
     project = {http://urafique.com/gaf/},
     pdf = {https://www.bmvc2020-conference.com/assets/papers/0055.pdf},
     month = sep
   }
   

8. Hadzic A, Christie G, Freeman J, Dismer A, Bullard S, Greiner A, Jacobs N, Mukherjee R. 2020. Estimating Displaced Populations from Overhead. In: IEEE International Geoscience and Remote Sensing Symposium (IGARSS). DOI: 10.1109/IGARSS39084.2020.9324617.
   bibtex | paper | doi | tweet

   close  

   @inproceedings{hadzic2020pop,
     annotation = {career,remote_sensing},
     author = {Hadzic, Armin and Christie, Gordon and Freeman, Jeffrey and Dismer, Amber and Bullard, Stevan and Greiner, Ashley and Jacobs, Nathan and Mukherjee, Ryan},
     booktitle = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
     title = {Estimating Displaced Populations from Overhead},
     tweet = {https://twitter.com/jacobsn/status/1278330759217139718},
     pdf = {https://arxiv.org/pdf/2006.14547},
     doi = {10.1109/IGARSS39084.2020.9324617},
     month = sep,
     year = {2020}
   }
   

9. Hammond TC, Xing X, Wang C, Ma D, Nho K, Crane PK, Elahi F, Ziegler DA, Liang G, Cheng Q, Yanckello LM, Jacobs N, Lin A-L. 2020. Beta-amyloid and tau drive early Alzheimer’s disease decline while glucose hypometabolism drives late decline. Communications Biology 3:352. DOI: 10.1038/s42003-020-1079-x.
   bibtex | paper | doi | tweet

   close  

   @article{hammond2020alzheimers,
     annotation = {career,medical,ugrad},
     title = {Beta-amyloid and tau drive early {Alzheimer's} disease decline while glucose hypometabolism drives late decline},
     author = {Hammond, Tyler C. and Xing, Xin and Wang, Chris and Ma, David and Nho, Kwangsik and Crane, Paul K. and Elahi, Fanny and Ziegler, David A. and Liang, Gongbo and Cheng, Qiang and Yanckello, Lucille M. and Jacobs, Nathan and Lin, Ai-Ling},
     doi = {10.1038/s42003-020-1079-x},
     journal = {Communications Biology},
     tweet = {https://twitter.com/jacobsn/status/1294304021180645382},
     pdf = {https://www.nature.com/articles/s42003-020-1079-x.pdf},
     number = {1},
     pages = {352},
     volume = {3},
     year = {2020},
     month = jul,
     note = {Impact factor: 6.268}
   }
   

10. Wang X, Liang G, Zhang Y, Blanton H, Bessinger Z, Jacobs N. 2020. Inconsistent Performance of Deep Learning Models on Mammogram Classification. Journal of the American College of Radiology. DOI: 10.1016/j.jacr.2020.01.006.
    bibtex | paper | doi

    close  

    @article{wang2020inconsistent,
      annotation = {career,medical},
      author = {Wang, Xiaoqin and Liang, Gongbo and Zhang, Yu and Blanton, Hunter and Bessinger, Zachary and Jacobs, Nathan},
      journal = {Journal of the American College of Radiology},
      note = {Impact factor: 3.785},
      title = {Inconsistent Performance of Deep Learning Models on Mammogram Classification},
      pdf = {https://www.sciencedirect.com/science/article/am/pii/S1546144020300284},
      doi = {10.1016/j.jacr.2020.01.006},
      year = {2020},
      month = jun
    }
    

11. Maretto RV, Fonseca LMG, Jacobs NB, Körting TS, Bendini HN, Parente LL. 2020. Spatio-Temporal Deep Learning Approach to Map Deforestation in Amazon Rainforest. IEEE Geoscience and Remote Sensing Letters 18:771–775. DOI: 10.1109/LGRS.2020.2986407.
    bibtex | paper | doi

    close  

    @article{maretto2020forest,
      annotation = {career,remote_sensing,spotlight},
      author = {Maretto, Raian V. and Fonseca, Leila M. G. and Jacobs, Nathan B. and K\"orting, Thales S. and Bendini, Hugo N. and Parente, Leandro L.},
      journal = {IEEE Geoscience and Remote Sensing Letters},
      note = {Impact factor: 3.534},
      doi = {10.1109/LGRS.2020.2986407},
      thumbnail = {/thumbnails/deforest.jpg},
      title = {Spatio-Temporal Deep Learning Approach to Map Deforestation in {Amazon} Rainforest},
      day = {28},
      month = apr,
      pdf = {https://ieeexplore.ieee.org/document/9080580},
      year = {2020},
      volume = {18},
      number = {5},
      pages = {771--775}
    }
    

12. Liang G, Wang X, Zhang Y, Jacobs N. 2020. Weakly-Supervised Self-Training for Breast Cancer Localization. In: International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). DOI: 10.1109/EMBC44109.2020.9176617.
    bibtex | paper | doi

    close  

    @inproceedings{liang2020weak,
      annotation = {career,medical},
      author = {Liang, Gongbo and Wang, Xiaoqin and Zhang, Yu and Jacobs, Nathan},
      booktitle = {International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},
      note = {(oral)},
      title = {Weakly-Supervised Self-Training for Breast Cancer Localization},
      doi = {10.1109/EMBC44109.2020.9176617},
      pdf = {https://drive.google.com/open?id=1MgoU-ZezxJ2MKzWuruvb9PxuyAl1bJqH},
      year = {2020}
    }
    

13. Blanton H, Grate S, Jacobs N. 2020. Surface Modeling for Airborne LiDAR. In: IEEE International Geoscience and Remote Sensing Symposium (IGARSS). DOI: 10.1109/IGARSS39084.2020.9323522.
    bibtex | paper | doi | tweet

    close  

    @inproceedings{blanton2020surface,
      annotation = {career,remote_sensing,lidar,pointcloud,3d,ugrad},
      author = {Blanton, Hunter and Grate, Sean and Jacobs, Nathan},
      booktitle = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
      tweet = {https://twitter.com/jacobsn/status/1294013341720084483},
      pdf = {https://drive.google.com/file/d/1crDl4cOd6OvOU4xhTGSlyY0Wk2QaFGxf/view?usp=share_link},
      title = {Surface Modeling for Airborne {LiDAR}},
      doi = {10.1109/IGARSS39084.2020.9323522},
      note = {(oral)},
      year = {2020}
    }
    

14. Blanton H, Greenwell C, Workman S, Jacobs N. 2020. Extending Absolute Pose Regression to Multiple Scenes. In: Joint Workshop on Long-Term Visual Localization, Visual Odometry and Geometric and Learning-based SLAM (CVPR Workshop). DOI: 10.1109/CVPRW50498.2020.00027.
    bibtex | paper | doi | tweet

    close  

    @inproceedings{blanton2020slam,
      author = {Blanton, Hunter and Greenwell, Connor and Workman, Scott and Jacobs, Nathan},
      booktitle = {Joint Workshop on Long-Term Visual Localization, Visual Odometry and Geometric and Learning-based {SLAM} ({CVPR} Workshop)},
      title = {Extending Absolute Pose Regression to Multiple Scenes},
      tweet = {https://twitter.com/jacobsn/status/1273422126280826881},
      pdf = {http://openaccess.thecvf.com/content_CVPRW_2020/papers/w3/Blanton_Extending_Absolute_Pose_Regression_to_Multiple_Scenes_CVPRW_2020_paper.pdf},
      video = {https://youtu.be/qb2h1zVizMY},
      doi = {10.1109/CVPRW50498.2020.00027},
      year = {2020},
      annotation = {career,3d,localization,calibation}
    }
    

15. Hadzic A, Blanton H, Song W, Chen M, Workman S, Jacobs N. 2020. RasterNet: Modeling Free-Flow Speed using LiDAR and Overhead Imagery. In: IEEE/ISPRS Workshop: Large Scale Computer Vision for Remote Sensing (EARTHVISION). DOI: 10.1109/CVPRW50498.2020.00112.
    bibtex | paper | website | doi | tweet

    close  

    @inproceedings{hadzic2020rasternet,
      author = {Hadzic, Armin and Blanton, Hunter and Song, Weilian and Chen, Mei and Workman, Scott and Jacobs, Nathan},
      tweet = {https://twitter.com/jacobsn/status/1273208772576423938},
      booktitle = {IEEE/ISPRS Workshop: Large Scale Computer Vision for Remote Sensing (EARTHVISION)},
      title = {{RasterNet}: Modeling Free-Flow Speed using LiDAR and Overhead Imagery},
      note = {Acceptance rate: 26\%},
      year = {2020},
      project = {https://mvrl.github.io/RasterNet},
      doi = {10.1109/CVPRW50498.2020.00112},
      video = {https://www.youtube.com/watch?v=IWKQoV6fXSk},
      pdf = {http://openaccess.thecvf.com/content_CVPRW_2020/html/w11/Hadzic_RasterNet_Modeling_Free-Flow_Speed_Using_LiDAR_and_Overhead_Imagery_CVPRW_2020_paper.html},
      annotation = {career,remote_sensing,3d,pointcloud,lidar,transportation}
    }
    

16. 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}
    }
    

17. 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.
    bibtex | paper | website | doi | tweet

    close  

    @inproceedings{workman2020dynamic,
      doi = {10.1109/CVPR42600.2020.01233},
      annotation = {google_crossview,career,remote_sensing,spotlight,transportation},
      author = {Workman, Scott and Jacobs, Nathan},
      booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
      date-modified = {2020-03-31 16:54:51 -0400},
      note = {Acceptance rate: 5.7\% (oral)},
      thumbnail = {/thumbnails/dynamic_road.jpg},
      title = {Dynamic Traffic Modeling from Overhead Imagery},
      tweet = {https://twitter.com/jacobsn/status/1272916897542086658},
      year = {2020},
      video = {https://www.youtube.com/watch?v=CNe5EEdyptE},
      project = {http://cs.uky.edu/%7escott/research/traffic/},
      arxiv = {https://arxiv.org/pdf/2012.10530},
      pdf = {http://openaccess.thecvf.com/content_CVPR_2020/papers/Workman_Dynamic_Traffic_Modeling_From_Overhead_Imagery_CVPR_2020_paper.pdf}
    }
    

18. Liang G, Zhang Y, Jacobs N. 2020. Neural Network Calibration for Medical Imaging Classification Using DCA Regularization. In: ICML 2020 workshop on Uncertainty and Robustness in Deep Learning (UDL).
    bibtex | paper

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    @inproceedings{liang2020udl,
      annotation = {career,medical},
      author = {Liang, Gongbo and Zhang, Yu and Jacobs, Nathan},
      booktitle = {ICML 2020 workshop on Uncertainty and Robustness in Deep Learning (UDL)},
      title = {Neural Network Calibration for Medical Imaging Classification Using {DCA} Regularization},
      pdf = {http://www.gatsby.ucl.ac.uk/~balaji/udl2020/accepted-papers/UDL2020-paper-137.pdf},
      year = {2020}
    }
    

19. Workman S, Rafique MU, Blanton H, Greenwell C, Jacobs N. 2020. Single Image Cloud Detection via Multi-Image Fusion. In: IEEE International Geoscience and Remote Sensing Symposium (IGARSS). DOI: 10.1109/IGARSS39084.2020.9323759.
    bibtex | paper | doi | tweet

    close  

    @inproceedings{workman2020cloud,
      annotation = {career,remote_sensing},
      author = {Workman, Scott and Rafique, M. Usman and Blanton, Hunter and Greenwell, Connor and Jacobs, Nathan},
      booktitle = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
      note = {(oral)},
      title = {Single Image Cloud Detection via Multi-Image Fusion},
      tweet = {https://twitter.com/jacobsn/status/1289301357019635712},
      pdf = {https://drive.google.com/open?id=1-1WOqk-LfEkT7AWvVCXVoi3Y90FgBePW},
      doi = {10.1109/IGARSS39084.2020.9323759},
      year = {2020}
    }
    

20. Hamraz H, Jacobs NB, Contreras MA, Clark CH. 2019. Deep Learning for Conifer/Deciduous Classification of Airborne LiDAR 3D Point Clouds Representing Individual Trees. ISPRS Journal of Photogrammetry and Remote Sensing 158:219–230. DOI: 10.1016/j.isprsjprs.2019.10.011.
    bibtex | paper | doi

    close  

    @article{hamraz2019deep,
      annotation = {career,remote_sensing,lidar,3d,pointcloud,spotlight},
      author = {Hamraz, Hamid and Jacobs, Nathan B. and Contreras, Marco A. and Clark, Chase H.},
      doi = {10.1016/j.isprsjprs.2019.10.011},
      issn = {0924-2716},
      journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
      note = {Impact factor: 6.946},
      pages = {219--230},
      pdf = {https://arxiv.org/pdf/1802.08872},
      thumbnail = {/thumbnails/forest.jpg},
      title = {{Deep Learning for Conifer/Deciduous Classification of
          Airborne LiDAR 3D Point Clouds Representing Individual Trees}},
      volume = {158},
      year = {2019},
      month = dec
    }
    

21. Song W, Salem T, Blanton H, Jacobs N. 2019. Remote Estimation of Free-Flow Speeds. In: IEEE International Geoscience and Remote Sensing Symposium (IGARSS). DOI: 10.1109/IGARSS.2019.8900286.
    bibtex | paper | doi | tweet

    close  

    @inproceedings{song2019speed,
      annotation = {career,remote_sensing,transportation},
      author = {Song, Weilian and Salem, Tawfiq and Blanton, Hunter and Jacobs, Nathan},
      booktitle = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
      thumbnail = {/thumbnails/freeflow_speed.jpg},
      title = {Remote Estimation of Free-Flow Speeds},
      tweet = {https://twitter.com/multimodal_lab/status/1352269143400132610},
      note = {(oral)},
      doi = {10.1109/IGARSS.2019.8900286},
      pdf = {https://arxiv.org/pdf/1906.10104},
      year = {2019}
    }
    

22. Zhu J, Nolte AM, Jacobs N, Ye M. 2019. Incorporating Machine Learning with LiDAR for Delineating Sinkholes. In: Kentucky Water Resources Annual Symposium.
    bibtex

    close  

    @inproceedings{zhu2019lidar,
      author = {Zhu, Junfeng and Nolte, Adam M and Jacobs, Nathan and Ye, Ming},
      booktitle = {Kentucky Water Resources Annual Symposium},
      title = {Incorporating Machine Learning with {LiDAR} for Delineating Sinkholes},
      year = {2019},
      annotation = {career,remote_sensing,lidar,3d}
    }
    

23. 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}
    }
    

24. Rafique MU, Jacobs N. 2019. Weakly Supervised Building Segmentation from Aerial Images. In: IEEE International Geoscience and Remote Sensing Symposium (IGARSS). DOI: 10.1109/IGARSS.2019.8898812.
    bibtex | paper | doi

    close  

    @inproceedings{rafique2019buildings,
      annotation = {career,remote_sensing},
      author = {Rafique, M. Usman and Jacobs, Nathan},
      booktitle = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
      title = {Weakly Supervised Building Segmentation from Aerial Images},
      doi = {10.1109/IGARSS.2019.8898812},
      pdf = {https://drive.google.com/open?id=1n0gW0GgBG-oognmrtcycD4wkaxc6dDqv},
      year = {2019}
    }
    

25. Rafique MU, Blanton H, Jacobs N. 2019. Weakly Supervised Fusion of Multiple Overhead Images. In: IEEE/ISPRS Workshop: Large Scale Computer Vision for Remote Sensing (EARTHVISION). DOI: 10.1109/CVPRW.2019.00189.
    bibtex | paper | doi | tweet

    close  

    @inproceedings{rafique2019fusion,
      annotation = {career,remote_sensing},
      author = {Rafique, M. Usman and Blanton, Hunter and Jacobs, Nathan},
      booktitle = {IEEE/ISPRS Workshop: Large Scale Computer Vision for Remote Sensing (EARTHVISION)},
      note = {Acceptance rate: 23.5\%},
      pdf = {http://openaccess.thecvf.com/content_CVPRW_2019/papers/EarthVision/Rafique_Weakly_Supervised_Fusion_of_Multiple_Overhead_Images_CVPRW_2019_paper.pdf},
      title = {Weakly Supervised Fusion of Multiple Overhead Images},
      tweet = {https://twitter.com/m_usmanrafique/status/1118352613752356864},
      doi = {10.1109/CVPRW.2019.00189},
      year = {2019}
    }
    

26. Jacobs N, Kraft A, Rafique MU, Sharma RD. 2018. A Weakly Supervised Approach for Estimating Spatial Density Functions from High-Resolution Satellite Imagery. In: ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL). DOI: 10.1145/3274895.3274934.
    bibtex | paper | doi | tweet | code

    close  

    @inproceedings{jacobs2018population,
      annotation = {career,spotlight,remote_sensing},
      author = {Jacobs, Nathan and Kraft, Adam and Rafique, M. Usman and Sharma, Ranti Dev},
      note = {Acceptance rate: 22.5\% (oral)},
      booktitle = {ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL)},
      code = {https://github.com/orbitalinsight/region-aggregation-public},
      tweet = {https://twitter.com/multimodal_lab/status/1354964074812092421},
      pdf = {https://arxiv.org/pdf/1810.09528},
      thumbnail = {/thumbnails/census.jpg},
      title = {A Weakly Supervised Approach for Estimating Spatial Density Functions from High-Resolution Satellite Imagery},
      doi = {10.1145/3274895.3274934},
      year = {2018}
    }
    

27. Schulter S, Zhai M, Jacobs N, Chandraker M. 2018. Learning to Look around Objects for Top-View Representations of Outdoor Scenes. In: European Conference on Computer Vision (ECCV). DOI: 10.1007/978-3-030-01267-0_48.
    bibtex | paper | doi

    close  

    @inproceedings{schulter2018,
      annotation = {career,spotlight,synthesis,transportation},
      author = {Schulter, Samuel and Zhai, Menghua and Jacobs, Nathan and Chandraker, Manmohan},
      booktitle = {European Conference on Computer Vision (ECCV)},
      note = {Acceptance rate: 31.8\%},
      date-modified = {2020-03-31 16:57:14 -0400},
      pdf = {https://arxiv.org/pdf/1803.10870},
      thumbnail = {/thumbnails/birdview.jpg},
      title = {Learning to Look around Objects for Top-View Representations of Outdoor Scenes},
      doi = {10.1007/978-3-030-01267-0_48},
      year = {2018}
    }
    

28. Zhai M, Salem T, Greenwell C, Workman S, Pless R, Jacobs N. 2018. Learning Geo-Temporal Image Features. In: British Machine Vision Conference (BMVC).
    bibtex | paper

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    @inproceedings{zhai2018geotemporal,
      annotation = {career,localization,amos,webcam,spotlight,social_media},
      author = {Zhai, Menghua and Salem, Tawfiq and Greenwell, Connor and Workman, Scott and Pless, Robert and Jacobs, Nathan},
      booktitle = {British Machine Vision Conference (BMVC)},
      note = {Acceptance rate: 29.5\%},
      pdf = {https://arxiv.org/pdf/1909.07499},
      thumbnail = {/thumbnails/when_where.jpg},
      title = {Learning Geo-Temporal Image Features},
      year = {2018}
    }
    

29. 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

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    @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}
    }
    

30. Salem T, Zhai M, Workman S, Jacobs N. 2018. A Multimodal Approach to Mapping Soundscapes. In: IEEE International Geoscience and Remote Sensing Symposium (IGARSS). DOI: 10.1109/IGARSS.2018.8517977.
    bibtex | paper | website | doi

    close  

    @inproceedings{salem2018soundscape,
      annotation = {career,remote_sensing,audio,social_media,cvsound,dataset_cvsound},
      author = {Salem, Tawfiq and Zhai, Menghua and Workman, Scott and Jacobs, Nathan},
      booktitle = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
      pdf = {https://tsalem.github.io/audio-mapping/papers/salem2018soundscape.pdf},
      project = {https://tsalem.github.io/audio-mapping/},
      doi = {10.1109/IGARSS.2018.8517977},
      thumbnail = {/thumbnails/audio_mapping.jpg},
      title = {A Multimodal Approach to Mapping Soundscapes},
      year = {2018}
    }
    

31. Song W, Workman S, Hadzic A, Souleyrette R, Green E, Chen M, Zhang X, Jacobs N. 2018. FARSA: Fully Automated Roadway Safety Assessment. In: IEEE Winter Conference on Applications of Computer Vision (WACV). DOI: 10.1109/WACV.2018.00063.
    bibtex | paper | doi | tweet

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    @inproceedings{song2018farsa,
      annotation = {career,spotlight,transportation},
      author = {Song, Weilian and Workman, Scott and Hadzic, Armin and Souleyrette, Reginald and Green, Eric and Chen, Mei and Zhang, Xu and Jacobs, Nathan},
      booktitle = {IEEE Winter Conference on Applications of Computer Vision (WACV)},
      date-modified = {2020-03-31 16:56:51 -0400},
      pdf = {https://arxiv.org/pdf/1901.06013},
      thumbnail = {/thumbnails/farsa.jpg},
      title = {{FARSA}: Fully Automated Roadway Safety Assessment},
      tweet = {https://twitter.com/multimodal_lab/status/1352267952154943488},
      doi = {10.1109/WACV.2018.00063},
      year = {2018}
    }
    

32. Vo N, Jacobs N, Hays J. 2017. Revisiting IM2GPS in the Deep Learning Era. In: IEEE International Conference on Computer Vision (ICCV). DOI: 10.1109/ICCV.2017.286.
    bibtex | paper | website | doi

    close  

    @inproceedings{vo2017revisit,
      annotation = {career,localization,social_media},
      author = {Vo, Nam and Jacobs, Nathan and Hays, James},
      booktitle = {IEEE International Conference on Computer Vision (ICCV)},
      pdf = {https://arxiv.org/pdf/1705.04838},
      project = {https://www.cc.gatech.edu/%7envo9/revisitingim2gps_iccv2017/},
      title = {Revisiting {IM2GPS} in the Deep Learning Era},
      note = {Acceptance rate: 28.9\%},
      doi = {10.1109/ICCV.2017.286},
      year = {2017}
    }
    

33. 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}
    }
    

34. 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.
    bibtex | paper | website | doi

    close  

    @inproceedings{workman2017scenic,
      annotation = {google_crossview,career,spotlight,remote_sensing,social_media},
      author = {Workman, Scott and Souvenir, Richard and Jacobs, Nathan},
      booktitle = {IEEE International Conference on Computer Vision (ICCV)},
      project = {http://cs.uky.edu/%7escott/research/scenicness/},
      thumbnail = {/thumbnails/beauty.jpg},
      title = {Understanding and Mapping Natural Beauty},
      doi = {10.1109/ICCV.2017.596},
      note = {Acceptance rate: 28.9\%},
      pdf = {https://arxiv.org/pdf/1612.03142},
      year = {2017}
    }
    

35. 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}
    }
    

Acknowledgements

This material is based upon work supported by the National Science Foundation under Grant No. IIS-1553116. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.