{"url":"/dataset/amstertime","name":"AmsterTime","full_name":"AmsterTime: A Visual Place Recognition Benchmark Dataset for Severe Domain Shift","description_markdown":"**AmsterTime** dataset offers a collection of 2,500 well-curated images matching the same scene from a street view matched to historical archival image data from Amsterdam city. The image pairs capture the same place with different cameras, viewpoints, and appearances. Unlike existing benchmark datasets, AmsterTime is directly crowdsourced in a GIS navigation platform (Mapillary). In turn, all the matching pairs are verified by a human expert to verify the correct matches and evaluate the human competence in the Visual Place Recognition (VPR) task for further references.\r\n\r\nThe properties of the dataset are summarized as:\r\n\r\n- 1200+ license-free images from the Amsterdam City Archive, representing urban places in the city of Amsterdam, captured in the past century by many photographers.\r\n- All archival queries are matched with street view images from Mapillary.\r\n- All matches are verified by architectural historians and Amsterdam inhabitants.\r\n- Image pairs are archival and street views capturing the same place with different cameras, time lags, structural changes, occlusion, viewpoint, appearance, and illuminations.\r\n- The dataset exhibits a domain shift between query and\r\n  the gallery due to significant difference between scanned archival and street view images.\r\n\r\nTwo sub-tasks are created on the dataset:\r\n\r\n- **Verification** is a binary classification (auxiliary) task to detect a pair of archival and street-view images of the same\r\n  place. The verification task for AmsterTime dataset has all of the crowdsourced image pairs as positive labeled, where the same number of negative samples are generated by randomly pairing archival and street-view images summing up to a total of 2,462 pairs in the verification task.\r\n\r\n- **Retrieval** is the main task corresponding to VPR, in which a given query image is matched with a set of gallery images. For the retrieval task, AmsterTime dataset offers 1231 query images where the leave-one-out set serves as the gallery images for each query.","description_withheld":null,"homepage":"https://github.com/seyrankhademi/AmsterTime","introduced_date":"2022-03-30","introduced_date_note":null,"introduced_by":{"paper":"/paper/amstertime-a-visual-place-recognition","title":"AmsterTime: A Visual Place Recognition Benchmark Dataset for Severe Domain Shift","first_author":"Burak Yildiz","url":null},"license":{"name":"CC0","url":"https://creativecommons.org/publicdomain/zero/1.0/"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Image Classification","url":"/task/image-classification","datasets_with_task":"/datasets/task/image-classification"},{"name":"Image Retrieval","url":"/task/image-retrieval","datasets_with_task":"/datasets/task/image-retrieval"},{"name":"Visual Place Recognition","url":"/task/visual-place-recognition","datasets_with_task":"/datasets/task/visual-place-recognition"}],"languages":[],"variants":["AmsterTime"],"data_loaders":[],"num_papers_in_archive":9,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/visual-place-recognition-on-amstertime","task":"Visual Place Recognition","dataset_variant":"AmsterTime","rows":8,"metrics":["Recall@1","Recall@10","Recall@5"],"first_row_in_archive_order":{"model":"FoL","paper":"/paper/focus-on-local-finding-reliable-1","metrics":{"Recall@1":"70.1","Recall@10":"90.0","Recall@5":"91.8"},"code_links":[{"title":"chenshunpeng/FoL","url":"https://github.com/chenshunpeng/FoL"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/image-retrieval-on-amstertime","task":"Image Retrieval","dataset_variant":"AmsterTime","rows":5,"metrics":["mAP"],"first_row_in_archive_order":{"model":"DINOv2 distilled (ViT-L/14 frozen)","paper":"/paper/dinov2-learning-robust-visual-features","metrics":{"mAP":"50.0"},"code_links":[{"title":"huggingface/transformers","url":"https://github.com/huggingface/transformers"},{"title":"facebookresearch/dinov2","url":"https://github.com/facebookresearch/dinov2"},{"title":"roboflow/rf-detr","url":"https://github.com/roboflow/rf-detr"},{"title":"open-edge-platform/training_extensions","url":"https://github.com/open-edge-platform/training_extensions"},{"title":"OML-Team/open-metric-learning","url":"https://github.com/OML-Team/open-metric-learning"},{"title":"leondgarse/keras_cv_attention_models","url":"https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/beit"},{"title":"fabio-sim/Depth-Anything-ONNX","url":"https://github.com/fabio-sim/Depth-Anything-ONNX"},{"title":"open-edge-platform/geti","url":"https://github.com/open-edge-platform/geti"},{"title":"facebookresearch/highrescanopyheight","url":"https://github.com/facebookresearch/highrescanopyheight"},{"title":"PaddlePaddle/PASSL","url":"https://github.com/PaddlePaddle/PASSL"},{"title":"beneroth13/dinov2","url":"https://github.com/beneroth13/dinov2"},{"title":"mohammedsb/dinov2formedical","url":"https://github.com/mohammedsb/dinov2formedical"},{"title":"marrlab/dinobloom","url":"https://github.com/marrlab/dinobloom"},{"title":"bespontaneous/proteus-pytorch","url":"https://github.com/bespontaneous/proteus-pytorch"},{"title":"ByungKwanLee/Causal-Unsupervised-Segmentation","url":"https://github.com/ByungKwanLee/Causal-Unsupervised-Segmentation"},{"title":"zhu-xlab/softcon","url":"https://github.com/zhu-xlab/softcon"},{"title":"birder/birder","url":"https://gitlab.com/birder/birder"},{"title":"gorkaydemir/DINOSAUR","url":"https://github.com/gorkaydemir/DINOSAUR"},{"title":"seatizendoi/dinovdeau","url":"https://github.com/seatizendoi/dinovdeau"},{"title":"BurguerJohn/global_perceptual_similarity_loss","url":"https://github.com/BurguerJohn/global_perceptual_similarity_loss"},{"title":"buyeah1109/KEN","url":"https://github.com/buyeah1109/KEN"},{"title":"JHKim-snu/PGA","url":"https://github.com/JHKim-snu/PGA"},{"title":"BurguerJohn/torch-felix","url":"https://github.com/BurguerJohn/torch-felix"},{"title":"2024-MindSpore-1/Code2","url":"https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/dinov2"},{"title":"buyeah1109/finc","url":"https://github.com/buyeah1109/finc"},{"title":"pwc-1/Paper-8","url":"https://github.com/pwc-1/Paper-8/tree/main/dinov2"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/image-classification-on-amstertime","task":"Image Classification","dataset_variant":"AmsterTime","rows":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"AP-GeM (ResNet-101)","paper":"/paper/amstertime-a-visual-place-recognition","metrics":{"Accuracy":"0.84"},"code_links":[{"title":"seyrankhademi/AmsterTime","url":"https://github.com/seyrankhademi/AmsterTime"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/query-based-adaptive-aggregation-for-multi","title":"Query-Based Adaptive Aggregation for Multi-Dataset Joint Training Toward Universal Visual Place Recognition","date":"2025-07-04","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/focus-on-local-finding-reliable-1","title":"Focus on Local: Finding Reliable Discriminative Regions for Visual Place Recognition","date":"2025-04-14","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/revisit-anything-visual-place-recognition-via","title":"Revisit Anything: Visual Place Recognition via Image Segment Retrieval","date":"2024-09-26","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":6,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/effovpr-effective-foundation-model","title":"EffoVPR: Effective Foundation Model Utilization for Visual Place Recognition","date":"2024-05-28","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/boq-a-place-is-worth-a-bag-of-learnable","title":"BoQ: A Place is Worth a Bag of Learnable Queries","date":"2024-05-12","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/eigenplaces-training-viewpoint-robust-models","title":"EigenPlaces: Training Viewpoint Robust Models for Visual Place Recognition","date":"2023-08-21","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":13,"samples_ran":11,"samples_unverified":2,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/dinov2-learning-robust-visual-features","title":"DINOv2: Learning Robust Visual Features without Supervision","date":"2023-04-14","rows_on_this_dataset":4,"code_links":26,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":46,"samples_ran":21,"samples_unverified":25,"pointer_only_for_licence":12,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/amstertime-a-visual-place-recognition","title":"AmsterTime: A Visual Place Recognition Benchmark Dataset for Severe Domain Shift","date":"2022-03-30","rows_on_this_dataset":2,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":5,"samples_harvested":73,"samples_ran":43,"samples_unverified":30,"pointer_only_for_licence":13,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}