title: GRE Suite: Geo-localization Inference via Fine-Tuned Vision-Language
    Models and Enhanced Reasoning Chains
summary: Recent advances in Visual Language Models (VLMs) have demonstrated
  exceptional performance in visual reasoning tasks. However, geo-localization
  presents unique challenges, requiring the extraction of multigranular visual
  cues from images and their integration with external world knowledge for
  systematic reasoning. Current approaches to geo-localization tasks often lack
  robust reasoning mechanisms and explainability, limiting their effectiveness.
  To address these limitations, we propose the Geo Reason Enhancement (GRE)
  Suite, a novel framework that augments VLMs with structured reasoning chains
  for accurate and interpretable location inference. The GRE Suite is
  systematically developed across three key dimensions: dataset, model, and
  benchmark. First, we introduce GRE30K, a high-quality geo-localization
  reasoning dataset designed to facilitate fine-grained visual and contextual
  analysis. Next, we present the GRE model, which employs a multi-stage reasoning
  strategy to progressively infer scene attributes, local details, and semantic
  features, thereby narrowing down potential geographic regions with enhanced
  precision. Finally, we construct the Geo Reason Evaluation Benchmark
  (GREval-Bench), a comprehensive evaluation framework that assesses VLMs across
  diverse urban, natural, and landmark scenes to measure both coarse-grained
  (e.g., country, continent) and fine-grained (e.g., city, street) localization
  performance. Experimental results demonstrate that GRE significantly
  outperforms existing methods across all granularities of geo-localization
  tasks, underscoring the efficacy of reasoning-augmented VLMs in complex
  geographic inference. Code and data will be released at
  https://github.com/Thorin215/GRE.
paper_authors._id: 6837cc29bbee677da741aba7
paper_authors.name: Chun Wang
paper_authors._id: 6837cc29bbee677da741aba8
paper_authors.name: Xiaoran Pan
paper_authors._id: 6837cc29bbee677da741aba9
paper_authors.name: Zihao Pan
paper_authors._id: 6837cc29bbee677da741abaa
paper_authors.name: Haofan Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-04T15:03:41.747Z
paper_authors._id: 637745113a63a2983ffbde13
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1669187672174-637745113a63a2983ffbde13.jpeg
paper_authors.fullname: Haofan Wang
paper_authors.type: user
paper_authors.user: wanghaofan
paper_authors._id: 6837cc29bbee677da741abab
paper_authors.name: Yiren Song
paper_title: GRE Suite: Geo-localization Inference via Fine-Tuned Vision-Language
    Models and Enhanced Reasoning Chains
paper_summary: Recent advances in Visual Language Models (VLMs) have demonstrated
  exceptional performance in visual reasoning tasks. However, geo-localization
  presents unique challenges, requiring the extraction of multigranular visual
  cues from images and their integration with external world knowledge for
  systematic reasoning. Current approaches to geo-localization tasks often lack
  robust reasoning mechanisms and explainability, limiting their effectiveness.
  To address these limitations, we propose the Geo Reason Enhancement (GRE)
  Suite, a novel framework that augments VLMs with structured reasoning chains
  for accurate and interpretable location inference. The GRE Suite is
  systematically developed across three key dimensions: dataset, model, and
  benchmark. First, we introduce GRE30K, a high-quality geo-localization
  reasoning dataset designed to facilitate fine-grained visual and contextual
  analysis. Next, we present the GRE model, which employs a multi-stage reasoning
  strategy to progressively infer scene attributes, local details, and semantic
  features, thereby narrowing down potential geographic regions with enhanced
  precision. Finally, we construct the Geo Reason Evaluation Benchmark
  (GREval-Bench), a comprehensive evaluation framework that assesses VLMs across
  diverse urban, natural, and landmark scenes to measure both coarse-grained
  (e.g., country, continent) and fine-grained (e.g., city, street) localization
  performance. Experimental results demonstrate that GRE significantly
  outperforms existing methods across all granularities of geo-localization
  tasks, underscoring the efficacy of reasoning-augmented VLMs in complex
  geographic inference. Code and data will be released at
  https://github.com/Thorin215/GRE.
paper_ai_summary: The GRE Suite enhances Visual Language Models with structured reasoning chains, improving geo-localization tasks through a multi-stage strategy and comprehensive evaluation benchmark.
paper_ai_keywords: Visual Language Models geo-localization reasoning chains GRE30K GRE model GREval-Bench multi-stage reasoning scene attributes local details semantic features coarse-grained localization fine-grained localization