GeoSearch: A Global Image Geolocalization Framework Integrating Web-Scale Reverse Image Search

This paper proposes the GeoSearch framework, which integrates web-scale reverse image search into the RAG pipeline. Through a two-layer filtering mechanism and web text evidence enhancement, it breaks through the coverage limitations of traditional fixed database methods, achieves better performance on the Im2GPS3k and YFCC4k benchmarks, and provides a feasible path for open-world image geolocalization.

The global image geolocalization task faces multiple challenges: diversity of global visual landscapes, changes in lighting/season/angle, and unbalanced geographic distribution (over-representation of popular areas and lack of remote areas). Traditional RAG-based methods rely on fixed geographic databases and cannot make accurate predictions when the query scene is not in the database.

The key breakthrough of GeoSearch lies in shifting geolocalization from a closed-world to an open-world paradigm: 1. No longer relying on fixed and limited reference databases, but using the entire Internet as a source of geographic knowledge; 2. Directly integrating web-scale reverse image search into the RAG pipeline to expand knowledge coverage.

The GeoSearch architecture includes three core components:

1. Multi-source candidate retrieval: Obtain candidate locations from both local geographic databases and web reverse searches simultaneously to cover more scenarios;
2. Web text evidence extraction: Extract text clues such as place names and landmark descriptions from related web pages to enhance the LMM reasoning context;
3. Two-layer filtering mechanism: Verify visual correspondence through image matching, then filter high-quality candidates via confidence gating to control network noise.

The study conducted strict evaluations on two benchmarks: Im2GPS3k (3000 test images) and YFCC4k. A leakage-prevention design was adopted to ensure that test images are not directly retrieved, avoiding data leakage affecting results. The results show that GeoSearch outperforms traditional fixed database methods on both benchmarks, proving that the gains from web search are real and generalizable.

Three key reasons for the effectiveness of web search:

1. Expanded geographic coverage: The Internet contains a huge number of geotagged images, covering from popular landmarks to remote areas;
2. Complementary text evidence: Web text provides disambiguation information such as place names, solving the problem of geographic distinction between visually similar scenes;
3. Dynamic knowledge update: No manual database updates are needed, and the latest geographic information is obtained automatically.

Current Limitations: Web search latency, network content noise, privacy-sensitive issues; Future Directions: Explore efficient search strategies to reduce latency, cross-language text processing, and use web page timestamps to achieve temporal geolocalization.

GeoSearch breaks through the coverage limitations of fixed databases and provides a feasible path for open-world geolocalization. Its idea of integrating Internet knowledge into vision-language tasks is expected to inspire broader research on open-world visual understanding.