Abstract

Given a picture taken somewhere in the world, automatic geo-localization of that image is a task that would be extremely useful e.g. for historical and forensic sciences, documentation purposes, orga- nization of the world’s photo material and also intelligence applications. While tremendous progress has been made over the last years in visual location recognition within a single city, localization in natural envi- ronments is much more difficult, since vegetation, illumination, seasonal changes make appearance-only approaches impractical. In this work, we target mountainous terrain and use digital elevation models to extract representations for fast visual database lookup. We propose an auto- mated approach for very large scale visual localization that can efficiently exploit visual information (contours) and geometric constraints (consis- tent orientation) at the same time. We validate the system on the scale of a whole country (Switzerland, 40 000km2 ) using a new dataset of more than 200 landscape query pictures with ground truth.