标题： 从像素到地点：大型语言模型图像地理定位能力的系统基准 显示英文标题

标题： From Pixels to Places: A Systematic Benchmark for Evaluating Image Geolocalization Ability in Large Language Models

摘要： 图像地理定位，即识别图像中描绘的地理位置，对于危机响应、数字取证和基于位置的情报应用非常重要。 尽管大型语言模型（LLMs）的最新进展为视觉推理提供了新的机会，但它们在图像地理定位方面的能力仍鲜有研究。 在本研究中，我们引入了一个名为IMAGEO-Bench的基准，用于系统评估准确性、距离误差、地理空间偏差和推理过程。 我们的基准包括三个多样化的数据集，涵盖全球街景、美国的感兴趣点（POIs）以及一个私人收藏的未见过的图像。 通过对10个最先进的LLMs进行实验，包括开源和闭源模型，我们揭示了明显的性能差异，闭源模型通常表现出更强的推理能力。 重要的是，我们发现LLMs存在地理空间偏差，它们在高资源地区（例如北美、西欧和加利福尼亚）表现更好，而在代表性不足的地区表现下降。 回归诊断表明，成功的地理定位主要依赖于识别城市环境、户外环境、街道级图像和可识别地标。 总体而言，IMAGEO-Bench为LLMs的空间推理能力提供了一个严格的观察视角，并为构建具有地理定位意识的人工智能系统提供了启示。 显示英文摘要

摘要： Image geolocalization, the task of identifying the geographic location depicted in an image, is important for applications in crisis response, digital forensics, and location-based intelligence. While recent advances in large language models (LLMs) offer new opportunities for visual reasoning, their ability to perform image geolocalization remains underexplored. In this study, we introduce a benchmark called IMAGEO-Bench that systematically evaluates accuracy, distance error, geospatial bias, and reasoning process. Our benchmark includes three diverse datasets covering global street scenes, points of interest (POIs) in the United States, and a private collection of unseen images. Through experiments on 10 state-of-the-art LLMs, including both open- and closed-source models, we reveal clear performance disparities, with closed-source models generally showing stronger reasoning. Importantly, we uncover geospatial biases as LLMs tend to perform better in high-resource regions (e.g., North America, Western Europe, and California) while exhibiting degraded performance in underrepresented areas. Regression diagnostics demonstrate that successful geolocalization is primarily dependent on recognizing urban settings, outdoor environments, street-level imagery, and identifiable landmarks. Overall, IMAGEO-Bench provides a rigorous lens into the spatial reasoning capabilities of LLMs and offers implications for building geolocation-aware AI systems.