title: NAVIG: Natural Language-guided Analysis with Vision Language Models for
    Image Geo-localization
summary: Image geo-localization is the task of predicting the specific location of an
  image and requires complex reasoning across visual, geographical, and cultural
  contexts. While prior Vision Language Models (VLMs) have the best accuracy at
  this task, there is a dearth of high-quality datasets and models for analytical
  reasoning. We first create NaviClues, a high-quality dataset derived from
  GeoGuessr, a popular geography game, to supply examples of expert reasoning
  from language. Using this dataset, we present Navig, a comprehensive image
  geo-localization framework integrating global and fine-grained image
  information. By reasoning with language, Navig reduces the average distance
  error by 14% compared to previous state-of-the-art models while requiring fewer
  than 1000 training samples. Our dataset and code are available at
  https://github.com/SparrowZheyuan18/Navig/.
paper_authors._id: 67b8760bf8311235c642d7a4
paper_authors.name: Zheyuan Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-21T14:42:43.700Z
paper_authors._id: 648c4af819bb04c06467189c
paper_authors.avatarUrl: /avatars/8b9372a233d4c00b555625fa7b5203e2.svg
paper_authors.fullname: Zheyuan Zhang
paper_authors.type: user
paper_authors.user: Zheyuan22
paper_authors._id: 67b8760bf8311235c642d7a5
paper_authors.name: Runze Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-24T09:21:43.993Z
paper_authors._id: 66482dea53317bdff335b006
paper_authors.avatarUrl: /avatars/5079ed975c5689739e1e8e4ae8a47a3d.svg
paper_authors.fullname: RUNZE LI
paper_authors.type: user
paper_authors.user: huggingCode11
paper_authors._id: 67b8760bf8311235c642d7a6
paper_authors.name: Tasnim Kabir
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-21T15:01:51.663Z
paper_authors._id: 6676067b8a4064c02b4ef6b7
paper_authors.avatarUrl: /avatars/319ff99740183793cab3045ae3bf1395.svg
paper_authors.fullname: Tasnim Kabir
paper_authors.type: user
paper_authors.user: TasnimKabir12
paper_authors._id: 67b8760bf8311235c642d7a7
paper_authors.name: Jordan Boyd-Graber
paper_title: NAVIG: Natural Language-guided Analysis with Vision Language Models for
    Image Geo-localization
paper_summary: Image geo-localization is the task of predicting the specific location of an
  image and requires complex reasoning across visual, geographical, and cultural
  contexts. While prior Vision Language Models (VLMs) have the best accuracy at
  this task, there is a dearth of high-quality datasets and models for analytical
  reasoning. We first create NaviClues, a high-quality dataset derived from
  GeoGuessr, a popular geography game, to supply examples of expert reasoning
  from language. Using this dataset, we present Navig, a comprehensive image
  geo-localization framework integrating global and fine-grained image
  information. By reasoning with language, Navig reduces the average distance
  error by 14% compared to previous state-of-the-art models while requiring fewer
  than 1000 training samples. Our dataset and code are available at
  https://github.com/SparrowZheyuan18/Navig/.
paper_ai_keywords: Vision Language Models (VLMs) NaviClues GeoGuessr CV expert reasoning Navig comprehensive image geo-localization framework global image information fine-grained image information Schwavig