Geolocation Estimation of Photos using a
Hierarchical Model and Scene Classification
Abstract. While the successful estimation of a photo’s geolocation enables a number of interesting applications, it is also a very challenging
task. Due to the complexity of the problem, most existing approaches
are restricted to specific areas, imagery, or worldwide landmarks. Only a
few proposals predict GPS coordinates without any limitations. In this
paper, we introduce several deep learning methods, which pursue the latter approach and treat geolocalization as a classification problem where
the earth is subdivided into geographical cells. We propose to exploit
hierarchical knowledge of multiple partitionings and additionally extract
and take the photo’s scene content into account, i.e., indoor, natural, or
urban setting etc. As a result, contextual information at different spatial resolutions as well as more specific features for various environmental
settings are incorporated in the learning process of the convolutional neural network. Experimental results on two benchmarks demonstrate the
effectiveness of our approach outperforming the state of the art while
using a significant lower number of training images and without relying
on retrieval methods that require an appropriate reference dataset