User-driven geolocation of untagged desert imagery using digital elevation models

Query skyline (top) and matched synthetic database skyline (bottom)

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 10,000km2 region of interest in a desert and show that in many cases, queries can be localized with precision as fine as 100m2.

Publication
In Computer Vision and Pattern Recognition 2013 Workshop on Visual Analysis and Geo-Localization of Large-Scale Imagery
Raphaël John Lamarre Townshend
Raphaël John Lamarre Townshend
PhD Fellow in Computer Science

Bringing machine learning to an atomic world.