{"url":"/dataset/coco-earthquake","name":"COCO Earthquake","full_name":null,"description_markdown":"**COCO Earthquake** is a dataset similar to Common Objects in Context (COCO) used for cracking segmentation. The images selected in the dataset are at various scales, and the tool referred to as the COCO Annotator is used to label cracks for training. In these labeled images, cracks are in yellow and background is in purple. Size of the training and labeling images is varied from 168×300 to 4600×3070. By excluding steel structures, 2,021 images are labeled when surface cracks appeared on structural or nonstructural materials at various scales.","description_withheld":null,"homepage":"","introduced_date":"2020-11-05","introduced_date_note":null,"introduced_by":{"paper":"/paper/end-to-end-deep-learning-methods-for","title":"End-to-end Deep Learning Methods for Automated Damage Detection in Extreme Events at Various Scales","first_author":"Yongsheng Bai","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Data Augmentation","url":"/task/data-augmentation","datasets_with_task":"/datasets/task/data-augmentation"}],"languages":[],"variants":["COCO Earthquake"],"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."}