Geolocation with Real Human Gameplay Data: A Large-Scale Dataset and
  Human-Like Reasoning Framework
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.
67b83a1f26e7d5f7cb0b7c9d
Zirui Song
claimed_verified
2025-02-21T09:58:04.247Z
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Zirui Song
user
Ziruibest
67b83a1f26e7d5f7cb0b7c9e
Jingpu Yang
admin_assigned
2025-02-21T15:06:10.877Z
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Jingpu Yang
user
yyds404
67b83a1f26e7d5f7cb0b7c9f
Yuan Huang
claimed_verified
2025-04-10T06:47:25.427Z
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Yuan Huang
user
ShirohAO
67b83a1f26e7d5f7cb0b7ca0
Jonathan Tonglet
admin_assigned
2025-02-21T15:06:50.179Z
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Jonathan Tonglet
user
saiga1420
67b83a1f26e7d5f7cb0b7ca1
Zeyu Zhang
claimed_verified
2025-02-27T12:54:53.512Z
64ec877bb93654d4ca5c92e9
https://cdn-avatars.huggingface.co/v1/production/uploads/64ec877bb93654d4ca5c92e9/GvHk_KSdE9Rhnk_o-NaZX.jpeg
Zeyu Zhang
user
SteveZeyuZhang
67b83a1f26e7d5f7cb0b7ca2
Tao Cheng
67b83a1f26e7d5f7cb0b7ca3
Meng Fang
67b83a1f26e7d5f7cb0b7ca4
Iryna Gurevych
67b83a1f26e7d5f7cb0b7ca5
Xiuying Chen
Geolocation with Real Human Gameplay Data: A Large-Scale Dataset and
  Human-Like Reasoning Framework
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.
A comprehensive geolocation framework with a large-scale dataset, novel reasoning method, and evaluation metric to improve geolocation accuracy and interpretability.
Large Vision Models multi-step reasoning GeoCoT GeoComp GeoEval large-scale dataset geolocation game platform geo-tagged locations contextual and spatial cues