UniGeo: A Multi-modal Large Language Model for Text-Guided Cross-View Geo-Localization

• URL: http://arxiv.org/abs/2608.26722v1
• Date: Thu, 27 Aug 2026 07:13:35 GMT
• Title: UniGeo: A Multi-modal Large Language Model for Text-Guided Cross-View Geo-Localization
• Abstract summary: Text-guided drone geo-localization aims to identify a target region in a large-scale image gallery from a natural-language description.<n>We propose UniGeo, a unified multimodal large language model (MLLM) for text-guided drone geo-localization.
• Score: 32.10571115517362
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Text-guided drone geo-localization aims to identify a target region in a large-scale image gallery from a natural-language description. Existing methods mainly formulate this task as direct matching between an open-ended text query and candidate images. However, incomplete queries and highly similar candidates often make global cross-modal matching insufficient for reliable fine-grained localization. We propose UniGeo, a unified multimodal large language model (MLLM) for text-guided drone geo-localization. Built on a shared vision-language framework, UniGeo jointly supports geo-semantic understanding, cross-view semantic generation, and candidate-level verification. Specifically, it establishes stable correspondences among local scene elements, spatial relations, and language descriptions through geo-semantic learning, and further models semantic mappings between drone and satellite views through cross-view generation. Based on these capabilities, a plug-and-play verification module performs fine-grained discrimination among highly confusable candidates. We further introduce a multi-stage training strategy that progressively learns geo-semantic understanding, cross-view generation, and candidate verification, improving adaptation to text-guided geo-localization. Experiments demonstrate consistent improvements across multiple retrieval backbones. On GeoText-1652, UniGeo improves R@10 and mAP by 13.59 and 2.83 percentage points, respectively, validating its effectiveness for fine-grained text-guided drone geo-localization.

Related papers

• Learning to Wander: Improving the Global Image Geolocation Ability of LMMs via Actionable Reasoning [72.13218601075958]
  We introduce textbfWanderBench, the first open access global geolocation benchmark for actionable geolocation reasoning in embodied scenarios.<n>We propose textbfGeoAoT (Action of Thought), a underlineGeolocation framework with underlineAction of underlineThough, which couples reasoning with embodied actions.<n>Experiments on 19 large multimodal models show that GeoAoT achieves superior fine-grained localization and stronger generalization in dynamic environments.
  arXiv  Detail & Related papers  (2026-03-11T06:24:10Z)
• GeoBridge: A Semantic-Anchored Multi-View Foundation Model Bridging Images and Text for Geo-Localization [36.20562550716618]
  Cross-view geo-localization infers a location by retrieving geo-tagged reference images that visually correspond to a query image.<n>Traditional satellite-centric paradigm limits robustness when high-resolution or up-to-date satellite imagery is unavailable.<n>We propose GeoBridge, a model that performs bidirectional matching across views and supports language-to-image retrieval.
  arXiv  Detail & Related papers  (2025-12-02T12:28:22Z)
• GraphGeo: Multi-Agent Debate Framework for Visual Geo-localization with Heterogeneous Graph Neural Networks [15.659980269049798]
  Visual geo-localization requires extensive geographic knowledge and sophisticated reasoning to determine image locations without GPS metadata.<n>Recent Large Vision-Language Models (LVLMs) enable direct location reasoning from image content, yet individual models struggle with diverse geographic regions and complex scenes.<n>We propose textbfGraphGeo, a multi-agent debate framework using heterogeneous graph neural networks for visual geo-localization.
  arXiv  Detail & Related papers  (2025-11-02T11:58:55Z)
• GeoVLM: Improving Automated Vehicle Geolocalisation Using Vision-Language Matching [6.8045687415659275]
  Cross-view geo-localisation identifies coarse geographical position of an automated vehicle by matching a ground-level image to a geo-tagged satellite image from a database.<n>Existing approaches reach high recall rates but they still fail to rank the correct image as the top match.<n>This paper proposes GeoVLM, a novel approach which uses the zero-shot capabilities of vision language models to enable cross-view geo-localisation.
  arXiv  Detail & Related papers  (2025-05-19T19:17:06Z)
• 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)
• G3: An Effective and Adaptive Framework for Worldwide Geolocalization Using Large Multi-Modality Models [40.69217368870192]
  We propose a novel framework for worldwide geolocalization based on Retrieval-Augmented Generation (RAG) G3 consists of three steps, i.e., Geo-alignment, Geo-diversification, and Geo-verification. Experiments on two well-established datasets verify the superiority of G3 compared to other state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-05-23T15:37:06Z)
• Towards Natural Language-Guided Drones: GeoText-1652 Benchmark with Spatial Relation Matching [60.645802236700035]
  Navigating drones through natural language commands remains challenging due to the dearth of accessible multi-modal datasets. We introduce GeoText-1652, a new natural language-guided geo-localization benchmark. This dataset is systematically constructed through an interactive human-computer process.
  arXiv  Detail & Related papers  (2023-11-21T17:52:30Z)
• GeoCLIP: Clip-Inspired Alignment between Locations and Images for Effective Worldwide Geo-localization [61.10806364001535]
  Worldwide Geo-localization aims to pinpoint the precise location of images taken anywhere on Earth. Existing approaches divide the globe into discrete geographic cells, transforming the problem into a classification task. We propose GeoCLIP, a novel CLIP-inspired Image-to-GPS retrieval approach that enforces alignment between the image and its corresponding GPS locations.
  arXiv  Detail & Related papers  (2023-09-27T20:54:56Z)
• 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)
• MGeo: Multi-Modal Geographic Pre-Training Method [49.78466122982627]
  We propose a novel query-POI matching method Multi-modal Geographic language model (MGeo) MGeo represents GC as a new modality and is able to fully extract multi-modal correlations for accurate query-POI matching. Our proposed multi-modal pre-training method can significantly improve the query-POI matching capability of generic PTMs.
  arXiv  Detail & Related papers  (2023-01-11T03:05:12Z)

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.