{"url":"/dataset/railsem19","name":"RailSem19","full_name":"RailSem19: A Dataset for Semantic Rail Scene Understanding","description_markdown":"RailSem19 offers 8500 unique images taken from a the ego-perspective of a rail vehicle (trains and trams). Extensive semantic annotations are provided, both geometry-based (rail-relevant polygons, all rails as polylines) and dense label maps with many Cityscapes-compatible road labels. Many frames show areas of intersection between road and rail vehicles (railway crossings, trams driving on city streets). RailSem19 is usefull for rail applications and road applications alike.\r\n\r\nImage credit: [https://wilddash.cc/railsem19](https://wilddash.cc/railsem19)","description_withheld":null,"homepage":"https://wilddash.cc/railsem19","introduced_date":"2019-06-16","introduced_date_note":null,"introduced_by":{"paper":"/paper/railsem19-a-dataset-for-semantic-rail-scene","title":"RailSem19: A Dataset for Semantic Rail Scene Understanding","first_author":"Oliver Zendel","url":null},"license":{"name":"Custom","url":"https://wilddash.cc/railsem19"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","datasets_with_task":"/datasets/task/semantic-segmentation"}],"languages":[],"variants":["RailSem19"],"data_loaders":[],"num_papers_in_archive":11,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"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."}