title: Around the World in 80 Timesteps: A Generative Approach to Global Visual
    Geolocation
summary: 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.
paper_authors._id: 6758588398dbc91f46c532b5
paper_authors.name: Nicolas Dufour
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-10T16:12:23.515Z
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paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/630652803aed65d34e98eee3/XG_PuVFA6ziGQZd3UUZSF.jpeg
paper_authors.fullname: Nicolas Dufour
paper_authors.type: user
paper_authors.user: nicolas-dufour
paper_authors._id: 6758588398dbc91f46c532b6
paper_authors.name: David Picard
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-10T16:17:26.420Z
paper_authors._id: 6385b65fde54faafdea46f0b
paper_authors.avatarUrl: /avatars/aba9aefa1dfa3d61ee3487b375a48ed5.svg
paper_authors.fullname: David
paper_authors.type: user
paper_authors.user: davidpicard
paper_authors._id: 6758588398dbc91f46c532b7
paper_authors.name: Vicky Kalogeiton
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-11T09:20:40.980Z
paper_authors._id: 6758941f1c12efeb74d88321
paper_authors.avatarUrl: /avatars/01117d1c0195897f9ecfd2ff764252ea.svg
paper_authors.fullname: Vicky Kalogeiton
paper_authors.type: user
paper_authors.user: vkalogeiton
paper_authors._id: 6758588398dbc91f46c532b8
paper_authors.name: Loic Landrieu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-11T13:39:17.757Z
paper_authors._id: 64f73dbe61afe1d3356b10cc
paper_authors.avatarUrl: /avatars/bacf3c01515d06dc5f21f6f732f52b01.svg
paper_authors.fullname: landrieu
paper_authors.type: user
paper_authors.user: loicland
paper_title: Around the World in 80 Timesteps: A Generative Approach to Global Visual
    Geolocation
paper_summary: 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.
paper_ai_summary: A generative geolocation approach using diffusion and Riemannian flow matching achieves state-of-the-art performance on visual geolocation tasks and introduces probabilistic geolocation with new metrics.
paper_ai_keywords: diffusion Riemannian flow matching denoising probabilistic visual geolocation visual geolocation benchmarks OpenStreetView-5M YFCC-100M iNat21