Geolocation is now a vital aspect of modern life, offering numerous benefits but also presenting serious privacy concerns. The advent of large vision-language models (LVLMs) with advanced image-processing capabilities introduces new risks, as these models can inadvertently reveal sensitive geolocation information. This paper presents the first in-depth study analyzing the challenges posed by traditional deep learning and LVLM-based geolocation methods. Our findings reveal that LVLMs can accurately determine geolocations from images, even without explicit geographic training. To address these challenges, we introduce \tool{}, an innovative framework that significantly enhances image-based geolocation accuracy. \tool{} employs a systematic chain-of-thought (CoT) approach, mimicking human geoguessing strategies by carefully analyzing visual and contextual cues such as vehicle types, architectural styles, natural landscapes, and cultural elements. Extensive testing on a dataset of 50,000 ground-truth data points shows that \tool{} outperforms both traditional models and human benchmarks in accuracy. It achieves an impressive average score of 4550.5 in the GeoGuessr game, with an 85.37\% win rate, and delivers highly precise geolocation predictions, with the closest distances as accurate as 0.3 km. Furthermore, our study highlights issues related to dataset integrity, leading to the creation of a more robust dataset and a refined framework that leverages LVLMs' cognitive capabilities to improve geolocation precision. These findings underscore \tool{}'s superior ability to interpret complex visual data, the urgent need to address emerging security vulnerabilities posed by LVLMs, and the importance of responsible AI development to ensure user privacy protection.

翻译：地理定位已成为现代生活的重要方面，在带来诸多便利的同时也引发了严重的隐私担忧。具备先进图像处理能力的视觉语言大模型（LVLMs）的出现带来了新的风险，这些模型可能无意中泄露敏感的地理位置信息。本文首次深入研究了传统深度学习和基于LVLM的地理定位方法所带来的挑战。研究发现，即使没有明确的地理训练，LVLMs也能从图像中准确判断地理位置。为应对这些挑战，我们提出了\tool{}——一个显著提升基于图像地理定位准确性的创新框架。该框架采用系统化的思维链（CoT）方法，通过细致分析车辆类型、建筑风格、自然景观和文化元素等视觉与上下文线索，模拟人类地理猜测策略。在包含50,000个真实数据点的数据集上进行广泛测试表明，\tool{}在准确性上超越了传统模型和人类基准。该框架在GeoGuessr游戏中取得了4550.5分的平均成绩，胜率达到85.37%，并能提供高度精确的地理位置预测，最接近距离精度达0.3公里。此外，本研究揭示了数据集完整性问题，由此构建了更稳健的数据集和优化框架，利用LVLMs的认知能力提升地理定位精度。这些发现彰显了\tool{}在解析复杂视觉数据方面的卓越能力，强调了解决LVLMs引发的安全漏洞的紧迫性，以及通过负责任的人工智能开发确保用户隐私保护的重要性。