title: Geolocation with Real Human Gameplay Data: A Large-Scale Dataset and
    Human-Like Reasoning Framework
summary: Geolocation, the task of identifying an image's location, requires complex
  reasoning and is crucial for navigation, monitoring, and cultural preservation.
  However, current methods often produce coarse, imprecise, and non-interpretable
  localization. A major challenge lies in the quality and scale of existing
  geolocation datasets. These datasets are typically small-scale and
  automatically constructed, leading to noisy data and inconsistent task
  difficulty, with images that either reveal answers too easily or lack
  sufficient clues for reliable inference. To address these challenges, we
  introduce a comprehensive geolocation framework with three key components:
  GeoComp, a large-scale dataset; GeoCoT, a novel reasoning method; and GeoEval,
  an evaluation metric, collectively designed to address critical challenges and
  drive advancements in geolocation research. At the core of this framework is
  GeoComp (Geolocation Competition Dataset), a large-scale dataset collected from
  a geolocation game platform involving 740K users over two years. It comprises
  25 million entries of metadata and 3 million geo-tagged locations spanning much
  of the globe, with each location annotated thousands to tens of thousands of
  times by human users. The dataset offers diverse difficulty levels for detailed
  analysis and highlights key gaps in current models. Building on this dataset,
  we propose Geographical Chain-of-Thought (GeoCoT), a novel multi-step reasoning
  framework designed to enhance the reasoning capabilities of Large Vision Models
  (LVMs) in geolocation tasks. GeoCoT improves performance by integrating
  contextual and spatial cues through a multi-step process that mimics human
  geolocation reasoning. Finally, using the GeoEval metric, we demonstrate that
  GeoCoT significantly boosts geolocation accuracy by up to 25% while enhancing
  interpretability.
paper_authors._id: 67b83a1f26e7d5f7cb0b7c9d
paper_authors.name: Zirui Song
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paper_authors.fullname: Zirui Song
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paper_authors.name: Jingpu Yang
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paper_authors.fullname: Jingpu Yang
paper_authors.type: user
paper_authors.user: yyds404
paper_authors._id: 67b83a1f26e7d5f7cb0b7c9f
paper_authors.name: Yuan Huang
paper_authors.status: claimed_verified
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paper_authors.fullname: Yuan Huang
paper_authors.type: user
paper_authors.user: ShirohAO
paper_authors._id: 67b83a1f26e7d5f7cb0b7ca0
paper_authors.name: Jonathan Tonglet
paper_authors.status: admin_assigned
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paper_authors._id: 641ed835f8c8b04c0ba7ac2a
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paper_authors.fullname: Jonathan Tonglet
paper_authors.type: user
paper_authors.user: saiga1420
paper_authors._id: 67b83a1f26e7d5f7cb0b7ca1
paper_authors.name: Zeyu Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-27T12:54:53.512Z
paper_authors._id: 64ec877bb93654d4ca5c92e9
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64ec877bb93654d4ca5c92e9/GvHk_KSdE9Rhnk_o-NaZX.jpeg
paper_authors.fullname: Zeyu Zhang
paper_authors.type: user
paper_authors.user: SteveZeyuZhang
paper_authors._id: 67b83a1f26e7d5f7cb0b7ca2
paper_authors.name: Tao Cheng
paper_authors._id: 67b83a1f26e7d5f7cb0b7ca3
paper_authors.name: Meng Fang
paper_authors._id: 67b83a1f26e7d5f7cb0b7ca4
paper_authors.name: Iryna Gurevych
paper_authors._id: 67b83a1f26e7d5f7cb0b7ca5
paper_authors.name: Xiuying Chen
paper_title: Geolocation with Real Human Gameplay Data: A Large-Scale Dataset and
    Human-Like Reasoning Framework
paper_summary: Geolocation, the task of identifying an image's location, requires complex
  reasoning and is crucial for navigation, monitoring, and cultural preservation.
  However, current methods often produce coarse, imprecise, and non-interpretable
  localization. A major challenge lies in the quality and scale of existing
  geolocation datasets. These datasets are typically small-scale and
  automatically constructed, leading to noisy data and inconsistent task
  difficulty, with images that either reveal answers too easily or lack
  sufficient clues for reliable inference. To address these challenges, we
  introduce a comprehensive geolocation framework with three key components:
  GeoComp, a large-scale dataset; GeoCoT, a novel reasoning method; and GeoEval,
  an evaluation metric, collectively designed to address critical challenges and
  drive advancements in geolocation research. At the core of this framework is
  GeoComp (Geolocation Competition Dataset), a large-scale dataset collected from
  a geolocation game platform involving 740K users over two years. It comprises
  25 million entries of metadata and 3 million geo-tagged locations spanning much
  of the globe, with each location annotated thousands to tens of thousands of
  times by human users. The dataset offers diverse difficulty levels for detailed
  analysis and highlights key gaps in current models. Building on this dataset,
  we propose Geographical Chain-of-Thought (GeoCoT), a novel multi-step reasoning
  framework designed to enhance the reasoning capabilities of Large Vision Models
  (LVMs) in geolocation tasks. GeoCoT improves performance by integrating
  contextual and spatial cues through a multi-step process that mimics human
  geolocation reasoning. Finally, using the GeoEval metric, we demonstrate that
  GeoCoT significantly boosts geolocation accuracy by up to 25% while enhancing
  interpretability.
paper_ai_summary: A comprehensive geolocation framework with a large-scale dataset, novel reasoning method, and evaluation metric to improve geolocation accuracy and interpretability.
paper_ai_keywords: Large Vision Models multi-step reasoning GeoCoT GeoComp GeoEval large-scale dataset geolocation game platform geo-tagged locations contextual and spatial cues