{"url":"/dataset/semantic-segmentation-dataset-on-earthquake","name":"Semantic segmentation dataset on earthquake damage","full_name":null,"description_markdown":"This dataset contains **547** social media images taken in the aftermath of various earthquakes. Each image is paired with a pixel-wise **semantic segmentation mask** that categorizes the scene into three classes:\r\n\r\n- **Undamaged structures**  \r\n- **Damaged structures**  \r\n- **Debris**\r\n\r\nThe segmentation masks were manually labeled and reviewed to support training and evaluation of machine learning models for pixel-level damage severity estimation.","description_withheld":null,"homepage":"https://github.com/Danrong430/Semantic-segmentation-dataset-on-earthquake-damage/","introduced_date":"2025-07-03","introduced_date_note":null,"introduced_by":{"paper":"/paper/from-pixels-to-damage-severity-estimating","title":"From Pixels to Damage Severity: Estimating Earthquake Impacts Using Semantic Segmentation of Social Media Images","first_author":"Danrong Zhang","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"2D Semantic Segmentation","url":"/task/2d-semantic-segmentation","datasets_with_task":"/datasets/task/2d-semantic-segmentation"}],"languages":[],"variants":["Semantic segmentation dataset on earthquake damage"],"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."}