User-Driven Geolocation of Untagged Desert Imagery Using Digital Elevation Models

Abstract

We propose a system for user-aided visual localization of desert imagery without the use of any metadata such as GPS readings, camera focal length, or field-of-view. The system makes use only of publicly available digital elevation models (DEMs) to rapidly and accurately locate photographs in non-urban environments such as deserts. Our system generates synthetic skyline views from a DEM and extracts stable concavity-based features from these skylines to form a database. To localize queries, a user manually traces the skyline on an input photograph. The skyline is automatically refined based on this estimate, and the same concavity-based features are extracted. We then apply a variety of geometrically constrained matching techniques to efficiently and accurately match the query skyline to a database skyline, thereby localizing the query image. We evaluate our system using a test set of 44 ground-truthed images over a 10, 000 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> region of interest in a desert and show that in many cases, queries can be localized with precision as fine as 100 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .

Cite

Text

Tzeng et al. "User-Driven Geolocation of Untagged Desert Imagery Using Digital Elevation Models." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2013. doi:10.1109/CVPRW.2013.42

Markdown

[Tzeng et al. "User-Driven Geolocation of Untagged Desert Imagery Using Digital Elevation Models." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2013.](https://mlanthology.org/cvprw/2013/tzeng2013cvprw-userdriven/) doi:10.1109/CVPRW.2013.42

BibTeX

@inproceedings{tzeng2013cvprw-userdriven,
  title     = {{User-Driven Geolocation of Untagged Desert Imagery Using Digital Elevation Models}},
  author    = {Tzeng, Eric and Zhai, Andrew and Clements, Matthew and Townshend, Raphael and Zakhor, Avideh},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2013},
  pages     = {237-244},
  doi       = {10.1109/CVPRW.2013.42},
  url       = {https://mlanthology.org/cvprw/2013/tzeng2013cvprw-userdriven/}
}