In this paper, we address the problem of global-scale image geolocation,
proposing a mixed classification-retrieval scheme. Unlike other methods that
strictly tackle the problem as a classification or retrieval task, we combine
the two practices in a unified solution leveraging the advantages of each
approach with two different modules. The first leverages the EfficientNet
architecture to assign images to a specific geographic cell in a robust way.
The second introduces a new residual architecture that is trained with
contrastive learning to map input images to an embedding space that minimizes
the pairwise geodesic distance of same-location images. For the final location
estimation, the two modules are combined with a search-within-cell scheme,
where the locations of most similar images from the predicted geographic cell
are aggregated based on a spatial clustering scheme. Our approach demonstrates
very competitive performance on four public datasets, achieving new
state-of-the-art performance in fine granularity scales, i.e., 15.0% at 1km
range on Im2GPS3k.

本文提出了一种混合分类 - 检索方案，其中利用 EfficientNet 架构将图像分配到特定的地理单元，并使用训练有素的残差架构将输入图像映射到一个嵌入空间，以最小化相同位置图像的配对测地线距离，然后将两个模块与空间聚类方案相结合，通过搜索 - 单元内的聚合确定最终位置估计。结果表明，该方法的性能非常竞争，在 4 个公共数据集上均能取得新的最优表现。