Around the World in 80 Timesteps: A Generative Approach to Global Visual
  Geolocation
Authors: name: Nicolas Dufour, name: David Picard, name: Vicky Kalogeiton, name: Loic Landrieu

Global visual geolocation predicts where an image was captured on Earth.
Since images vary in how precisely they can be localized, this task inherently
involves a significant degree of ambiguity. However, existing approaches are
deterministic and overlook this aspect. In this paper, we aim to close the gap
between traditional geolocalization and modern generative methods. We propose
the first generative geolocation approach based on diffusion and Riemannian
flow matching, where the denoising process operates directly on the Earth's
surface. Our model achieves state-of-the-art performance on three visual
geolocation benchmarks: OpenStreetView-5M, YFCC-100M, and iNat21. In addition,
we introduce the task of probabilistic visual geolocation, where the model
predicts a probability distribution over all possible locations instead of a
single point. We introduce new metrics and baselines for this task,
demonstrating the advantages of our diffusion-based approach. Codes and models
will be made available.