Pinpoint: Grounded Worldwide Image Geolocation via Cross-Source Retrieval and Reranking

Researchers have developed Pinpoint, a new image geolocation system that combines internet photos and street-view imagery to determine where a photograph was taken. The system uses a two-stage pipeline of retrieval and reranking to achieve state-of-the-art accuracy on standard benchmarks without relying on large language models. Pinpoint outperforms previous methods across all metrics on datasets for both internet photos and street-view imagery.

arXiv:2606.04133v1 Announce Type: new Abstract: Image geolocation aims to estimate where a photograph was taken from its visual content. At worldwide scale, this remains challenging because visual evidence is often ambiguous, diverse, and unevenly distributed. Prior work has typically treated geolocation of ordinary internet photos and street-view imagery as separate tasks, despite their complementary strengths: internet photos better match the appearance distribution of user-captured queries, while street-view imagery provides denser, geographically grounded coverage. We present Pinpoint, a retrieve-and-rerank architecture that combines both sources in a coarse-to-fine pipeline. A contrastive image-GPS embedder is trained on both user-uploaded Flickr photos and street-view imagery, learning a shared image-GPS embedding space that is used to retrieve candidate locations. An attention-based reranker then rescores retrieved candidates by combining candidate-level visual and GPS features with cross-source evidence from nearby locations to ground the prediction. Unlike recent prior work, Pinpoint does not rely on multimodal large-language models, making inference faster and more reproducible. Pinpoint achieves state-of-the-art results across all metrics on standard benchmarks for internet photos IM2GPS3k and YFCC4k and street-view imagery OSV-5M .