GRE Suite: Geo-localization Inference via Fine-Tuned Vision-Language
  Models and Enhanced Reasoning Chains
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.
6837cc29bbee677da741aba7
Chun Wang
6837cc29bbee677da741aba8
Xiaoran Pan
6837cc29bbee677da741aba9
Zihao Pan
6837cc29bbee677da741abaa
Haofan Wang
claimed_verified
2025-06-04T15:03:41.747Z
637745113a63a2983ffbde13
https://cdn-avatars.huggingface.co/v1/production/uploads/1669187672174-637745113a63a2983ffbde13.jpeg
Haofan Wang
user
wanghaofan
6837cc29bbee677da741abab
Yiren Song
GRE Suite: Geo-localization Inference via Fine-Tuned Vision-Language
  Models and Enhanced Reasoning Chains
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.
The GRE Suite enhances Visual Language Models with structured reasoning chains, improving geo-localization tasks through a multi-stage strategy and comprehensive evaluation benchmark.
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