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。

LLMGeo: 大规模语言模型在野外图像地理定位中的基准测试

LLMGeo: Benchmarking Large Language Models on Image Geolocation  In-the-wild

Reliable image geolocation is crucial for several applications, ranging from
social media geo-tagging to fake news detection. State-of-the-art geolocation
methods surpass human performance on the task of geolocation estimation from
images. However, no method assesses the suitability of an image for this task,
which results in unreliable and erroneous estimations for images containing no
geolocation clues. In this paper, we define the task of image localizability,
i.e. suitability of an image for geolocation, and propose a selective
prediction methodology to address the task. In particular, we propose two novel
selection functions that leverage the output probability distributions of
geolocation models to infer localizability at different scales. Our selection
functions are benchmarked against the most widely used selective prediction
baselines, outperforming them in all cases. By abstaining from predicting
non-localizable images, we improve geolocation accuracy from 27.8% to 70.5% at
the city-scale, and thus make current geolocation models reliable for
real-world applications.

本文提出了一个选择性预测方法来解决图片适合地理定位问题，其中包括两个选型函数，能够将不可定位的图片排除预测，大大提高了城市规模的地理定位精度，使得现有的地理定位模型在实际应用中更加可靠。