Geolocation with Real Human Gameplay Data: A Large-Scale Dataset and
  Human-Like Reasoning Framework
Authors: name: Zirui Song, name: Jingpu Yang, name: Yuan Huang, name: Jonathan Tonglet, name: Zeyu Zhang, name: Tao Cheng, name: Meng Fang, name: Iryna Gurevych, name: Xiuying Chen

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.