{"url":"/sota/photo-geolocation-estimation-on-yfcc4k","task":{"name":"Photo geolocation estimation","url":"/task/photo-geolocation-estimation","note":null},"dataset":{"name":"YFCC4k","url":null},"category":"Computer Vision","categories":["Adversarial","Computer Vision","Graphs"],"category_note":null,"description":"**Photo geolocation estimation** is task of estimate or classify the geolocation from photos on world map.","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["Median Error (km)","Street (1 km)","City (25 km)","Region (200 km)","Country (750 km)","Continent (2500 km)"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"Median Error (km)":"lower","Street (1 km)":null,"City (25 km)":null,"Region (200 km)":null,"Country (750 km)":null,"Continent (2500 km)":null}},"counts":{"rows":4,"rows_with_code":2,"rows_with_paper_page":4,"rows_dated":4,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"PIGEOTTO","metrics":{"City (25 km)":"23.7","Continent (2500 km)":"77.7","Country (750 km)":"62.2","Median Error (km)":"383.0","Region (200 km)":"40.6","Street (1 km)":"10.4"},"uses_additional_data":false,"paper_date":"2023-07-11","paper":"/paper/pigeon-predicting-image-geolocations","paper_url":"https://arxiv.org/abs/2307.05845v6","paper_title":"PIGEON: Predicting Image Geolocations","code":"https://github.com/LukasHaas/PIGEON","n_code_links":1,"syntology":{"n_ran":1,"n_unverified":1,"n_samples":2,"n_pointer_only_licence":2}},{"rank_in_archive_order":2,"model":"GeoRanker","metrics":{"City (25 km)":"43.54","Continent (2500 km)":"82.45","Country (750 km)":"69.79","Median Error (km)":"/","Region (200 km)":"54.32","Street (1 km)":"32.94"},"uses_additional_data":false,"paper_date":"2025-05-19","paper":"/paper/georanker-distance-aware-ranking-for","paper_url":"https://arxiv.org/abs/2505.13731v1","paper_title":"GeoRanker: Distance-Aware Ranking for Worldwide Image Geolocalization","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":3,"model":"G3","metrics":{"City (25 km)":"35.89","Continent (2500 km)":"78.15","Country (750 km)":"64.26","Median Error (km)":"/","Region (200 km)":"46.98","Street (1 km)":"23.99"},"uses_additional_data":false,"paper_date":"2024-05-23","paper":"/paper/g3-an-effective-and-adaptive-framework-for","paper_url":"https://arxiv.org/abs/2405.14702v2","paper_title":"G3: An Effective and Adaptive Framework for Worldwide Geolocalization Using Large Multi-Modality Models","code":"https://github.com/applied-machine-learning-lab/g3","n_code_links":1,"syntology":{"n_ran":3,"n_unverified":5,"n_samples":8,"n_pointer_only_licence":0}},{"rank_in_archive_order":4,"model":"[L]kNN, σ = 4","metrics":{"City (25 km)":"5.7","Continent (2500 km)":"42.0","Country (750 km)":"23.5","Region (200 km)":"11.0","Street (1 km)":"2.3"},"uses_additional_data":false,"paper_date":"2017-05-13","paper":"/paper/revisiting-im2gps-in-the-deep-learning-era","paper_url":"http://arxiv.org/abs/1705.04838v1","paper_title":"Revisiting IM2GPS in the Deep Learning Era","code":null,"n_code_links":0,"syntology":null}],"since_archive":{"claim":"Results that newer papers report for their own method, placed here by Syntology. A model pointed at the cell in the paper's own table; the number was read from that cell and checked against this leaderboard's metric, dataset, split and scale; an independent check that saw this leaderboard's other rows and every other leaderboard on the same dataset accepted it. 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Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":2,"rows_with_any_sample_ran":2,"distinct_papers_with_graph_line":2,"distinct_papers_with_any_sample_ran":2,"samples_over_distinct_papers":{"n_ran":4,"n_unverified":6,"n_samples":10,"n_pointer_only_licence":2,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":4,"n_unverified":6,"n_samples":10,"n_pointer_only_licence":2,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}