GeoChain: Multimodal Chain-of-Thought for Geographic Reasoning

• URL: http://arxiv.org/abs/2506.00785v1
• Date: Sun, 01 Jun 2025 02:24:46 GMT
• Title: GeoChain: Multimodal Chain-of-Thought for Geographic Reasoning
• Authors: Sahiti Yerramilli, Nilay Pande, Rynaa Grover, Jayant Sravan Tamarapalli,
• Abstract summary: GeoChain is a benchmark for evaluating step-by-step geographic reasoning in multimodal large language models (MLLMs)<n>It pairs each image with a 21-step chain-of-thought (CoT) question sequence (over 30 million Q&A pairs)<n>These sequences guide models from coarse attributes to fine-grained localization across four reasoning categories.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This paper introduces GeoChain, a large-scale benchmark for evaluating step-by-step geographic reasoning in multimodal large language models (MLLMs). Leveraging 1.46 million Mapillary street-level images, GeoChain pairs each image with a 21-step chain-of-thought (CoT) question sequence (over 30 million Q&A pairs). These sequences guide models from coarse attributes to fine-grained localization across four reasoning categories - visual, spatial, cultural, and precise geolocation - annotated by difficulty. Images are also enriched with semantic segmentation (150 classes) and a visual locatability score. Our benchmarking of contemporary MLLMs (GPT-4.1 variants, Claude 3.7, Gemini 2.5 variants) on a diverse 2,088-image subset reveals consistent challenges: models frequently exhibit weaknesses in visual grounding, display erratic reasoning, and struggle to achieve accurate localization, especially as the reasoning complexity escalates. GeoChain offers a robust diagnostic methodology, critical for fostering significant advancements in complex geographic reasoning within MLLMs.

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