ReGeo: A Direct Regression Approach for Global Image Geo-Localization
Tobias Rothlin, Mitra Purandare · 2025
This paper presents a novel approach to geolocalization, a task that aims to predict the latitude and longitude of an image based on its visual content. Traditional methods in this domain often rely on databases, complex pipelines or large-scale image classification networks. In contrast, we propose a direct regression approach that simplifies the process by predicting the geographic coordinates directly from the image features. We leverage a pre-trained Vision Transformer(ViT) model, specifically the ViT of a pre-trained CLIP model, for feature extraction and introduce a regression head for coordinate prediction. Various configurations, including pre-training and task-specific adaptations, are tested and evaluated resulting in our model called ReGeo. Experimental results show that ReGeo offers competitive performance compared to the existing approaches, despite being simpler and with minimal supporting code. ReGeo consistently achieves the best performance at the continent level across the GWS15K, IM2GPS3K, and IM2GPS datasets, and even outperforms GeoCLIP at the country level on GWS15K. Notably, GWS15K exhibits a significant distribution shift compared to ReGeo's training data, highlighting the model's strong generalization capabilities. On the holdout dataset, ReGeo localizes 32 % of 5,000 images at the city level, and achieves increasingly higher accuracy at broader radii—88.08% at the regional level, 96.86 % at the country level, and 99.34 % at the continent level-demonstrating its effectiveness for mid-range to coarse-grained geo-localization tasks.