aiqbench analysis
This research demonstrates that integrating textual supervision significantly improves the geospatial reasoning capabilities of vision-language models, addressing systematic gaps in spatial accuracy across diverse image clusters. By validating language as an effective complementary modality for encoding spatial context, the study positions multimodal learning as a critical pathway for advancing geospatial AI applications. The findings offer a concrete technical direction for optimizing foundation models like LLaVA and Qwen for tasks requiring precise image geolocation and spatial understanding.
Textual Supervision Enhances Geospatial Representations in Vision-Language Models
arXiv:2606.07172v1 Announce Type: cross Abstract: Geospatial understanding is a critical yet underexplored dimension in the development of machine learning systems for tasks such as image geolocation and spatial reasoning. In this work, we analyze the geospatial representations acquired by three model families: vision-only architectures (e.g., ViT), vision-language models (e.g., CLIP), and large-scale multimodal foundation models (e.g., LLaVA, Qwen, and Gemma). By evaluating across image clusters, including people, landmarks, and everyday objects, grouped based on the degree of localizability, we reveal systematic gaps in spatial accuracy and show that textual supervision enhances the learning of geospatial representations. Our findings suggest the role of language as an effective complementary modality for encoding spatial context and multimodal learning as a key direction for advancing geospatial AI.
Abstract:Geospatial understanding is a critical yet underexplored dimension in the development of machine learning systems for tasks such as image geolocation and spatial reasoning. In this work, we analyze the geospatial representations acquired by three model families: vision-only architectures (e.g., ViT), vision-language models (e.g., CLIP), and large-scale multimodal foundation models (e.g., LLaVA, Qwen, and Gemma). By evaluating across image clusters, including people, landmarks, and everyday objects, grouped based on the degree of localizability, we reveal systematic gaps in spatial accuracy and show that textual supervision enhances the learning of geospatial representations. Our findings suggest the role of language as an effective complementary modality for encoding spatial context and multimodal learning as a key direction for advancing geospatial AI.
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
arXiv:2606.07172 [cs.CV]
(or arXiv:2606.07172v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.07172 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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