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Learning and Using Models of Geo-Temporal Appearance


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. PDF Vo N., Jacobs N., Hays J. 2017. Revisiting IM2GPS in the Deep Learning Era. In: IEEE International Conference on Computer Vision (ICCV). website bibtex
  2. Workman S., Zhai M., Crandall D., Jacobs N. 2017. A Unified Model for Near/Remote Sensing. In: IEEE International Conference on Computer Vision (ICCV). website bibtex
  3. Workman S., Souvenir R., Jacobs N. 2017. Understanding and Mapping Natural Beauty. In: IEEE International Conference on Computer Vision (ICCV). website bibtex
  4. PDF Zhai M., Bessinger Z., Workman S., Jacobs N. 2017. Predicting Ground-Level Scene Layout from Aerial Imagery. In: IEEE Computer Vision and Pattern Recognition (CVPR). bibtex


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.