{"url":"/dataset/pavementscapes","name":"Pavementscapes","full_name":null,"description_markdown":"**Pavementscapes** is a large-scale dataset to develop and evaluate methods for pavement damage segmentation. It is comprised of 4,000 images with a resolution of 1024×2048, which have been recorded in the real-world pavement inspection projects with 15 different pavements. A total of 8,680 damage instances are manually labeled with six damage classes at the pixel level.","description_withheld":null,"homepage":"","introduced_date":"2022-07-24","introduced_date_note":null,"introduced_by":{"paper":"/paper/pavementscapes-a-large-scale-hierarchical","title":"Pavementscapes: a large-scale hierarchical image dataset for asphalt pavement damage segmentation","first_author":"Zheng Tong","url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["Pavementscapes"],"data_loaders":[],"num_papers_in_archive":1,"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."}