Automatic Outdoor Image Geolocation with Focal Modulation Networks
Fabio Murgese, Gerard Alcaina, Mehmet Oğuz Mülâyim, Jesús Cerquides, Jose Luis Fernandez-Marquez · Frontiers in artificial intelligence and applications · 2022
We address the problem of estimating a photo’s geographical location. Success in this estimation enables many impactful applications, like facilitating Disaster Management circumstances. However, this is also a very challenging task. Due to the complexity of the problem, we restrict the area of geolocation to a single city, treating geolocation as a classification problem where the districts of a city are the classes to be distinguished. In this paper, we exploit the Focal Modulation Network that is proven to perform effectively and efficiently in visual modeling for real-world applications. Experimental results on two diverse datasets, crawled from online sources, show the effectiveness of our approach. We can geolocate correctly more than two-thirds of test images from the larger dataset and about one-third from an experimental training dataset of a ten-times smaller size.