Image geolocation is a critical task in various image-understanding
applications. However, existing methods often fail when analyzing challenging,
in-the-wild images. Inspired by the exceptional background knowledge of
multimodal language models, we systematically evaluate their geolocation
capabilities using a novel image dataset and a comprehensive evaluation
framework. We first collect images from various countries via Google Street
View. Then, we conduct training-free and training-based evaluations on
closed-source and open-source multi-modal language models. we conduct both
training-free and training-based evaluations on closed-source and open-source
multimodal language models. Our findings indicate that closed-source models
demonstrate superior geolocation abilities, while open-source models can
achieve comparable performance through fine-tuning.

利用多模态语言模型，我们系统评估其图片地理定位能力，并通过新的图像数据集和全面的评估框架进行训练及非训练的评估。结果表明，闭源模型展示出更好的地理定位能力，而开源模型通过微调可达到相当的性能。