Textual Supervision Enhances Geospatial Representations in Vision-Language Models
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Computer Science > Computer Vision and Pattern Recognition
Title:Textual Supervision Enhances Geospatial Representations in Vision-Language Models
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.
| Comments: | Accepted at ICML 2026 |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.07172 [cs.CV] |
| (or arXiv:2606.07172v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2606.07172
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Marcelo Locatelli [view email][v1] Fri, 5 Jun 2026 11:40:13 UTC (11,910 KB)
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