Textual Supervision Enhances Geospatial Representations in Vision-Language Models

• URL: http://arxiv.org/abs/2606.07172v1
• Date: Fri, 05 Jun 2026 11:40:13 GMT
• Title: Textual Supervision Enhances Geospatial Representations in Vision-Language Models
• Abstract summary: Geospatial understanding is a critical yet underexplored dimension in the development of machine learning systems.<n>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)
• Score: 47.5986642837928
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models [12.200359585933766]
  Recent advances in self-supervised learning have given rise to Earth Observation Foundation Models (EOFMs)<n>This paper calls for a paradigm shift toward joint Spatial Representation Learning (SRL) in an unified embedding space that integrate perception with vector-based reasoning.<n>We contend that such integration is essential for developing next-generation geospatial AI systems capable of more accurate, interpretable, and grounded understanding of the Earth.
  arXiv  Detail & Related papers  (2026-06-01T15:21:58Z)
• Where Do Vision-Language Models Fail? World Scale Analysis for Image Geolocalization [3.713195826948382]
  Vision-Language Models (VLMs) have demonstrated strong zero-shot reasoning capabilities across multimodal tasks.<n>We present a systematic evaluation of multiple state-of-the-art VLMs for country-level image geolocalization using ground-view imagery only.
  arXiv  Detail & Related papers  (2026-04-17T17:09:14Z)
• Global Cross-Modal Geo-Localization: A Million-Scale Dataset and a Physical Consistency Learning Framework [13.268717213871147]
  Cross-modal Geo-localization (CMGL) matches ground-level text descriptions with geo-tagged aerial imagery.<n>We introduce CORE, the first million-scale dataset dedicated to global CMGL.<n>We propose a physical-law-aware network (PLANET) for cross-modal geo-localization.
  arXiv  Detail & Related papers  (2026-03-09T15:27:19Z)
• GeoVista: Web-Augmented Agentic Visual Reasoning for Geolocalization [53.080882980294795]
  Current research on agentic visual reasoning enables deep multimodal understanding but primarily focuses on image manipulation tools.<n>In this work, we revisit the geolocalization task, which requires not only nuanced visual grounding but also web search to confirm or refine hypotheses.<n>Since existing geolocalization benchmarks fail to meet the need for high-resolution imagery and the localization challenge for deep agentic reasoning, we curate GeoBench.<n>We propose GeoVista, an agentic model that seamlessly integrates tool invocation within the reasoning loop, including an image-zoom-in tool to magnify regions of interest and a web-search tool to retrieve related
  arXiv  Detail & Related papers  (2025-11-19T18:59:22Z)
• OmniGeo: Towards a Multimodal Large Language Models for Geospatial Artificial Intelligence [51.0456395687016]
  multimodal large language models (LLMs) have opened new frontiers in artificial intelligence.<n>We propose a MLLM (OmniGeo) tailored to geospatial applications.<n>By combining the strengths of natural language understanding and spatial reasoning, our model enhances the ability of instruction following and the accuracy of GeoAI systems.
  arXiv  Detail & Related papers  (2025-03-20T16:45:48Z)
• Swarm Intelligence in Geo-Localization: A Multi-Agent Large Vision-Language Model Collaborative Framework [51.26566634946208]
  We introduce smileGeo, a novel visual geo-localization framework. By inter-agent communication, smileGeo integrates the inherent knowledge of these agents with additional retrieved information. Results show that our approach significantly outperforms current state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-08-21T03:31:30Z)
• Towards Vision-Language Geo-Foundation Model: A Survey [65.70547895998541]
  Vision-Language Foundation Models (VLFMs) have made remarkable progress on various multimodal tasks. This paper thoroughly reviews VLGFMs, summarizing and analyzing recent developments in the field.
  arXiv  Detail & Related papers  (2024-06-13T17:57:30Z)
• GeoGLUE: A GeoGraphic Language Understanding Evaluation Benchmark [56.08664336835741]
  We propose a GeoGraphic Language Understanding Evaluation benchmark, named GeoGLUE. We collect data from open-released geographic resources and introduce six natural language understanding tasks. We pro vide evaluation experiments and analysis of general baselines, indicating the effectiveness and significance of the GeoGLUE benchmark.
  arXiv  Detail & Related papers  (2023-05-11T03:21:56Z)
• Visual and Object Geo-localization: A Comprehensive Survey [11.120155713865918]
  Geo-localization refers to the process of determining where on earth some entity' is located. This paper provides a comprehensive survey of geo-localization involving images, which involves either determining from where an image has been captured (Image geo-localization) or geo-locating objects within an image (Object geo-localization) We will provide an in-depth study, including a summary of popular algorithms, a description of proposed datasets, and an analysis of performance results to illustrate the current state of each field.
  arXiv  Detail & Related papers  (2021-12-30T20:46:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.