GeoChain: Multimodal Chain-of-Thought for Geographic Reasoning
Sahiti Yerramilli, Nilay Pande, Rynaa Grover, Jayant Sravan Tamarapalli
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 frontier MLLMs 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.
BibTeX
@inproceedings{emnlp2025_geochainmultimod,
title = {GeoChain: Multimodal Chain-of-Thought for Geographic Reasoning},
author = {Sahiti Yerramilli and Nilay Pande and Rynaa Grover and Jayant Sravan Tamarapalli},
booktitle = {EMNLP 2025},
year = {2025}
}