You Are Here: Geolocation by Embedding Maps and Images
Abstract
We present a novel approach to geolocalising panoramic images on a 2-D cartographic map based on learning a low dimensional embedded space, which allows a comparison between an image captured at a location and local neighbourhoods of the map. The representation is not sufficiently discriminatory to allow localisation from a single image, but when concatenated along a route, localisation converges quickly, with over 90% accuracy being achieved for routes of around 200m in length when using Google Street View and Open Street Map data. The method generalises a previous fixed semantic feature based approach and achieves significantly higher localisation accuracy and faster convergence.
Cite
Text
Samano et al. "You Are Here: Geolocation by Embedding Maps and Images." Proceedings of the European Conference on Computer Vision (ECCV), 2020. doi:10.1007/978-3-030-58592-1_30Markdown
[Samano et al. "You Are Here: Geolocation by Embedding Maps and Images." Proceedings of the European Conference on Computer Vision (ECCV), 2020.](https://mlanthology.org/eccv/2020/samano2020eccv-you/) doi:10.1007/978-3-030-58592-1_30BibTeX
@inproceedings{samano2020eccv-you,
title = {{You Are Here: Geolocation by Embedding Maps and Images}},
author = {Samano, Noe and Zhou, Mengjie and Calway, Andrew},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2020},
doi = {10.1007/978-3-030-58592-1_30},
url = {https://mlanthology.org/eccv/2020/samano2020eccv-you/}
}