You Are Here: Geolocation by Embedding Maps and Images

Noe Samano; Mengjie Zhou; Andrew Calway

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arXiv · 1911.08797

You Are Here: Geolocation by Embedding Maps and Images

Noe Samano·Mengjie Zhou·Andrew Calway

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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.

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BibTeX RIS

Noe Samano, Mengjie Zhou, Andrew Calway. 2019-11-20. You Are Here: Geolocation by Embedding Maps and Images. https://arxiv.org/abs/1911.08797

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