Geolocation estimation of photos using a hierarchical model and scene classification

26Citations
Citations of this article
142Readers
Mendeley users who have this article in their library.

This article is free to access.

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.

Cite

CITATION STYLE

APA

Müller-Budack, E., Pustu-Iren, K., & Ewerth, R. (2018). Geolocation estimation of photos using a hierarchical model and scene classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11216 LNCS, pp. 575–592). Springer Verlag. https://doi.org/10.1007/978-3-030-01258-8_35

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free