{"url":"/dataset/aiderv2","name":"AIDERV2","full_name":"Aerial Image Dataset for Emergency Response Applications (version 2)","description_markdown":"The dataset contains aerial images containing three commonly occurring natural disasters\r\nearthquake/collapsed buildings, flood, wildfire/fire, and a normal class; do not reflect any disaster. It consist of 167723 aerial images divided into 4 classes. The dataset is an extension of the AIDER dataset (Aerial Image Dataset for Emergency Response Applications). \r\n\r\nif you use this dataset please cite the following publications:\r\n \r\n[1] Shianios, D., Kyrkou, C., Kolios, P.S. (2023). A Benchmark and Investigation of Deep-Learning-Based Techniques for Detecting Natural Disasters in Aerial Images. In: Tsapatsoulis, N., et al. Computer Analysis of Images and Patterns. CAIP 2023. Lecture Notes in Computer Science, vol 14185. Springer, Cham. https://doi.org/10.1007/978-3-031-44240-7_24\r\nLink: https://link.springer.com/chapter/10.1007/978-3-031-44240-7_24\r\n \r\n[2] D. Shianios, P. Kolios, C. Kyrkou, \"DiRecNetV2: A Transformer-Enhanced Network for Aerial Disaster Recognition\", SN Computer Science, 2024 (Accepted to Appear)","description_withheld":null,"homepage":"https://zenodo.org/records/10891054","introduced_date":"2024-03-28","introduced_date_note":null,"introduced_by":null,"license":{"name":"CC BY","url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Image Classification","url":"/task/image-classification","datasets_with_task":"/datasets/task/image-classification"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["AIDERV2"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/image-classification-on-aiderv2","task":"Image Classification","dataset_variant":"AIDERV2","rows":1,"metrics":["Test F1 score"],"first_row_in_archive_order":{"model":"TakuNet FP=16","paper":"/paper/takunet-an-energy-efficient-cnn-for-real-time","metrics":{"Test F1 score":"0.958"},"code_links":[{"title":"danielrossi1/takunet","url":"https://github.com/danielrossi1/takunet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/takunet-an-energy-efficient-cnn-for-real-time","title":"TakuNet: an Energy-Efficient CNN for Real-Time Inference on Embedded UAV systems in Emergency Response Scenarios","date":"2025-01-10","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"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."}