Mountain Peak Identification in Visual Content Based on Coarse Digital Elevation Models

Abstract

We present a method for the identification of mountain peaks in geo-tagged photos. The key tenet is to perform an edge-based matching between the visual content of each photo and a terrain view synthesized from a Digital Elevation Model (DEM). The latter is generated as if a virtual observer is located at the coordinates indicated by the geo-tag. The key property of the method is the ability to reach a highly accurate estimation of the position of mountain peaks with a coarse resolution DEM available in the corresponding geographical area, which is sampled at a spatial resolution between 30m and 90m. This is the case for publicly available DEMs that cover almost the totality of the Earth surface (such as SRTM CGIAR and ASTER GDEM). The method is fully unsupervised, thus it can be applied to the analysis of massive amounts of user generated content available, e.g., on Flickr and Panoramio. We evaluated our method on a dataset of manually annotated images of mountain landscapes, containing peaks of the Italian and Swiss Alps. Our results show that it is possible to accurately identify the peaks in 75.0% of the cases. This result increases to 81.6% when considering only photos with mountain slopes far from the observer.