Textual Supervision Enhances Geospatial Representations in Vision-Language Models

2026-06-05

AI Summary

This study addresses the challenge of enhancing geospatial representation capabilities in machine learning systems for image geolocation and spatial reasoning tasks. By systematically evaluating pure vision models (e.g., ViT) against vision–language multimodal foundation models—including CLIP, LLaVA, Qwen, and Gemma—on image clusters grouped by localizability, the work reveals the critical role of textual supervision in geospatial representation learning. The findings demonstrate that incorporating linguistic modalities significantly strengthens a model’s ability to encode spatial context, effectively bridging the systematic gap in spatial accuracy observed in existing approaches. These insights underscore the potential of multimodal learning to advance geospatial artificial intelligence.

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.

Problem

• geospatial understanding
• image geolocation
• spatial reasoning
• vision-language models
• multimodal learning

Innovation

• textual supervision
• geospatial representations
• vision-language models
• multimodal learning
• spatial reasoning