{"url":"/dataset/isbda","name":"ISBDA","full_name":null,"description_markdown":"Consists of user-generated aerial videos from social media with annotations of instance-level building damage masks. This provides the first benchmark for quantitative evaluation of models to assess building damage using aerial videos.\r\n\r\nSource: [MSNet: A Multilevel Instance Segmentation Network for Natural Disaster Damage Assessment in Aerial Videos](/paper/msnet-a-multilevel-instance-segmentation)","description_withheld":null,"homepage":"https://github.com/zgzxy001/MSNET","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/msnet-a-multilevel-instance-segmentation","title":"MSNet: A Multilevel Instance Segmentation Network for Natural Disaster Damage Assessment in Aerial Videos","first_author":"Xiaoyu Zhu","url":null},"license":null,"modalities":[],"tasks":[{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","datasets_with_task":"/datasets/task/semantic-segmentation"},{"name":"Instance Segmentation","url":"/task/instance-segmentation","datasets_with_task":"/datasets/task/instance-segmentation"},{"name":"Region Proposal","url":"/task/region-proposal","datasets_with_task":"/datasets/task/region-proposal"}],"languages":[],"variants":["ISBDA"],"data_loaders":[{"repo":"https://github.com/zgzxy001/MSNET","url":"https://github.com/zgzxy001/MSNET","frameworks":[]}],"num_papers_in_archive":5,"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."}