The appearance of the world varies dramatically not only from place to place but also from hour to hour and month to month. Every day billions of images capture this complex relationship, many of which are associated with precise time and location metadata. We propose to use these images to construct a global-scale, dynamic map of visual appearance attributes. Such a map enables fine-grained understanding of the expected appearance at any geographic location and time. Our approach integrates dense overhead imagery with location and time metadata into a general framework capable of mapping a wide variety of visual attributes. A key feature of our approach is that it requires no manual data annotation. We demonstrate how this approach can support various applications, including image-driven mapping, image geolocalization, and metadata verification.
|Number of pages||10|
|Journal||Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition|
|State||Published - 2020|
|Event||2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020 - Virtual, Online, United States|
Duration: Jun 14 2020 → Jun 19 2020
Bibliographical noteFunding Information:
Acknowledgements: We gratefully acknowledge the financial support of an NSF CAREER grant (IIS-1553116), the University of Kentucky Center for Computational Sciences, and a Google Faculty Research Award. Thanks to Armin Hadzic for helpful feedback on the manuscript.
© 2020 IEEE
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition