{"url":"/dataset/york-urban-line-segment-database","name":"York Urban Line Segment Database","full_name":null,"description_markdown":"The York Urban Line Segment Database is a compilation of 102 images (45 indoor, 57 outdoor) of urban environments consisting mostly of scenes from the campus of York University and downtown Toronto, Canada. The images are 640 x 480 in size and have been taken with a calibrated Panasonic Lumix DMC-LC80 digital camera.\r\n\r\nEach image in the database has been hand-labelled to identify the set of line segments satisfying the “Manhattan assumption” (Coughlan & Yuille 2003), i.e., the set of line segments that conform to the 3D orthogonal frame of the urban environment.\r\n\r\nThese hand-labelled data have been used to identify the three Manhattan vanishing points in each image and from these to identify the Euler angles relating the camera frame to the Manhattan frame of the scene.\r\n\r\nThe database provides the original images, camera calibration parameters, ground truth line segments, and estimated Manhattan frame relative to the camera for each image.\r\n\r\nSource: [York Urban Line Segment Database](https://www.elderlab.yorku.ca/resources/york-urban-line-segment-database-information/)","description_withheld":null,"homepage":"https://www.elderlab.yorku.ca/resources/york-urban-line-segment-database-information/","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":{"name":"Unknown","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Horizon Line Estimation","url":"/task/horizon-line-estimation","datasets_with_task":"/datasets/task/horizon-line-estimation"},{"name":"Line Segment Detection","url":"/task/line-segment-detection","datasets_with_task":"/datasets/task/line-segment-detection"}],"languages":[],"variants":["York Urban Dataset","York Urban Line Segment Database"],"data_loaders":[],"num_papers_in_archive":17,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/line-segment-detection-on-york-urban-dataset","task":"Line Segment Detection","dataset_variant":"York Urban Dataset","rows":16,"metrics":["sAP5","sAP10","sAP15","FH"],"first_row_in_archive_order":{"model":"LINEA-L","paper":"/paper/linea-fast-and-accurate-line-detection-using","metrics":{"sAP10":"34.9","sAP15":"37.3","sAP5":"30.9"},"code_links":[{"title":"SebastianJanampa/LINEA","url":"https://github.com/SebastianJanampa/LINEA"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/horizon-line-estimation-on-york-urban-dataset","task":"Horizon Line Estimation","dataset_variant":"York Urban Dataset","rows":4,"metrics":["AUC (horizon error)"],"first_row_in_archive_order":{"model":"V","paper":"/paper/a-contrario-horizon-first-vanishing-point","metrics":{"AUC (horizon error)":"95.35"},"code_links":[{"title":"alexvonduar/V","url":"https://github.com/alexvonduar/V"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/linea-fast-and-accurate-line-detection-using","title":"LINEA: Fast and Accurate Line Detection Using Scalable Transformers","date":"2025-05-22","rows_on_this_dataset":4,"code_links":1,"syntology":null},{"paper":"/paper/dt-lsd-deformable-transformer-based-line","title":"DT-LSD: Deformable Transformer-based Line Segment Detection","date":"2024-11-20","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/lsdnet-trainable-modification-of-lsd","title":"LSDNet: Trainable Modification of LSD Algorithm for Real-Time Line Segment Detection","date":"2022-09-10","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/towards-real-time-and-light-weight-line","title":"Towards Light-weight and Real-time Line Segment Detection","date":"2021-06-01","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/elsd-efficient-line-segment-detector-and","title":"ELSD: Efficient Line Segment Detector and Descriptor","date":"2021-04-29","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/fully-convolutional-line-parsing","title":"Fully Convolutional Line Parsing","date":"2021-04-22","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":19,"samples_ran":2,"samples_unverified":17,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/line-segment-detection-using-transformers","title":"Line Segment Detection Using Transformers without Edges","date":"2021-01-06","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/ulsd-unified-line-segment-detection-across","title":"ULSD: Unified Line Segment Detection across Pinhole, Fisheye, and Spherical Cameras","date":"2020-11-06","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/tp-lsd-tri-points-based-line-segment-detector-1","title":"TP-LSD: Tri-Points Based Line Segment Detector","date":"2020-09-11","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/deep-hough-transform-line-priors","title":"Deep Hough-Transform Line Priors","date":"2020-07-18","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/holistically-attracted-wireframe-parsing","title":"Holistically-Attracted Wireframe Parsing","date":"2020-03-03","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/190503246","title":"End-to-End Wireframe Parsing","date":"2019-05-08","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":14,"samples_ran":1,"samples_unverified":13,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/a-contrario-horizon-first-vanishing-point","title":"A-Contrario Horizon-First Vanishing Point Detection Using Second-Order Grouping Laws","date":"2018-09-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/deep-learning-for-vanishing-point-detection","title":"Deep Learning for Vanishing Point Detection Using an Inverse Gnomonic Projection","date":"2017-07-08","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/mcmlsd-a-dynamic-programming-approach-to-line","title":"MCMLSD: A Dynamic Programming Approach to Line Segment Detection","date":"2017-07-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/detecting-vanishing-points-using-global-image","title":"Detecting Vanishing Points using Global Image Context in a Non-Manhattan World","date":"2016-08-19","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/horizon-lines-in-the-wild","title":"Horizon Lines in the Wild","date":"2016-04-07","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":3,"samples_harvested":34,"samples_ran":4,"samples_unverified":30,"pointer_only_for_licence":0,"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."}