LocationAgent: A Hierarchical Agent for Image Geolocation via Decoupling Strategy and Evidence from Parametric Knowledge

• URL: http://arxiv.org/abs/2601.19155v1
• Date: Tue, 27 Jan 2026 03:40:03 GMT
• Title: LocationAgent: A Hierarchical Agent for Image Geolocation via Decoupling Strategy and Evidence from Parametric Knowledge
• Authors: Qiujun Li, Zijin Xiao, Xulin Wang, Zhidan Ma, Cheng Yang, Haifeng Li,
• Abstract summary: Image geolocation aims to infer capture locations based on visual content.<n>Existing methods typically internalize location knowledge and reasoning patterns into static memory.<n>We propose a Hierarchical Localization Agent, called LocationAgent.<n>Our core philosophy is to retain hierarchical reasoning logic within the model while offloading the verification of geographic evidence to external tools.
• Score: 6.433767853804077
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image geolocation aims to infer capture locations based on visual content. Fundamentally, this constitutes a reasoning process composed of \textit{hypothesis-verification cycles}, requiring models to possess both geospatial reasoning capabilities and the ability to verify evidence against geographic facts. Existing methods typically internalize location knowledge and reasoning patterns into static memory via supervised training or trajectory-based reinforcement fine-tuning. Consequently, these methods are prone to factual hallucinations and generalization bottlenecks in open-world settings or scenarios requiring dynamic knowledge. To address these challenges, we propose a Hierarchical Localization Agent, called LocationAgent. Our core philosophy is to retain hierarchical reasoning logic within the model while offloading the verification of geographic evidence to external tools. To implement hierarchical reasoning, we design the RER architecture (Reasoner-Executor-Recorder), which employs role separation and context compression to prevent the drifting problem in multi-step reasoning. For evidence verification, we construct a suite of clue exploration tools that provide diverse evidence to support location reasoning. Furthermore, to address data leakage and the scarcity of Chinese data in existing datasets, we introduce CCL-Bench (China City Location Bench), an image geolocation benchmark encompassing various scene granularities and difficulty levels. Extensive experiments demonstrate that LocationAgent significantly outperforms existing methods by at least 30\% in zero-shot settings.

Related papers

• OpenEarthAgent: A Unified Framework for Tool-Augmented Geospatial Agents [68.85365034738534]
  We introduce a unified framework for developing tool-augmented geospatial agents trained on satellite imagery, natural-language queries, and detailed reasoning traces.<n>The training pipeline relies on supervised fine-tuning over structured reasoning trajectories, aligning the model with verified multistep tool interactions.<n>The accompanying corpus comprises 14,538 training and 1,169 evaluation instances, with more than 100K reasoning steps in the training split and over 7K reasoning steps in the evaluation split.
  arXiv  Detail & Related papers  (2026-02-19T18:59:54Z)
• GeoAgent: Learning to Geolocate Everywhere with Reinforced Geographic Characteristics [91.17301794848025]
  This paper presents GeoAgent, a model capable of reasoning closely with humans and deriving fine-grained address conclusions.<n>Previous RL-based methods have achieved breakthroughs in performance and interpretability but still remain concerns because of their reliance on AI-generated chain-of-thought (CoT) data and training strategies.
  arXiv  Detail & Related papers  (2026-02-13T04:48:05Z)
• SpotAgent: Grounding Visual Geo-localization in Large Vision-Language Models through Agentic Reasoning [31.665287327579026]
  SpotAgent is a framework that formalizes geo-localization into an agentic reasoning process.<n>It actively explores and verifies visual cues by leveraging external tools (e.g., web search, maps) through a ReAct diagram.<n>It achieves state-of-the-art performance, effectively mitigating hallucinations while delivering precise and verifiable geo-localization.
  arXiv  Detail & Related papers  (2026-02-10T06:57:12Z)
• Thinking on Maps: How Foundation Model Agents Explore, Remember, and Reason Map Environments [10.485672302572368]
  Map environments provide a fundamental medium for representing spatial structure. Understanding how foundation model (FM) agents understand and act in such environments is critical for enabling reliable map-based reasoning and applications.<n>We propose an interactive evaluation framework to analyze how FM agents explore, remember, and reason in symbolic map environments.
  arXiv  Detail & Related papers  (2025-12-30T23:04:29Z)
• 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)
• From Pixels to Places: A Systematic Benchmark for Evaluating Image Geolocalization Ability in Large Language Models [14.178064117544082]
  Image geolocalization is important for applications in crisis response, digital forensics, and location-based intelligence.<n>Recent advances in large language models (LLMs) offer new opportunities for visual reasoning.<n>We introduce a benchmark called IMAGEO-Bench that systematically evaluates accuracy, distance error, geospatial bias, and reasoning process.
  arXiv  Detail & Related papers  (2025-08-03T06:04:33Z)
• Recognition through Reasoning: Reinforcing Image Geo-localization with Large Vision-Language Models [47.98900725310249]
  New pipeline constructs a reasoning-oriented geo-localization dataset, MP16-Reason, using diverse social media images.<n>GLOBE incorporates task-specific rewards that jointly enhance localizability assessment, visual-cue reasoning, and geolocation accuracy.<n>Results demonstrate that GLOBE outperforms state-of-the-art open-source LVLMs on geo-localization tasks.
  arXiv  Detail & Related papers  (2025-06-17T16:07:58Z)
• 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)
• Unsupervised Metric Relocalization Using Transform Consistency Loss [66.19479868638925]
  Training networks to perform metric relocalization traditionally requires accurate image correspondences. We propose a self-supervised solution, which exploits a key insight: localizing a query image within a map should yield the same absolute pose, regardless of the reference image used for registration. We evaluate our framework on synthetic and real-world data, showing our approach outperforms other supervised methods when a limited amount of ground-truth information is available.
  arXiv  Detail & Related papers  (2020-11-01T19:24:27Z)

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.