{"url":"/dataset/firerisk","name":"FireRisk","full_name":"FireRisk: A Remote Sensing Dataset for Fire Risk Assessment","description_markdown":"In this work, we propose a novel remote sensing dataset, FireRisk, consisting of 7 fire risk classes with a total of 91 872 labelled images for fire risk assessment. This remote sensing dataset is labelled with the fire risk classes supplied by the Wildfire Hazard Potential (WHP) raster dataset, and remote sensing images are collected using the National Agriculture Imagery Program (NAIP), a high-resolution remote sensing imagery program. On FireRisk, we present benchmark performance for supervised and self-supervised representations, with Masked Autoencoders (MAE) pre-trained on ImageNet1k achieving the highest classification accuracy, 65.29%.","description_withheld":null,"homepage":"","introduced_date":"2023-03-13","introduced_date_note":null,"introduced_by":{"paper":"/paper/firerisk-a-remote-sensing-dataset-for-fire","title":"FireRisk: A Remote Sensing Dataset for Fire Risk Assessment with Benchmarks Using Supervised and Self-supervised Learning","first_author":"Shuchang Shen","url":null},"license":{"name":"CC BY-NC","url":"https://creativecommons.org/licenses/by-nc/4.0/"},"modalities":[{"name":"Graphs","url":"/datasets/modality/graphs"}],"tasks":[{"name":"Image Classification","url":"/task/image-classification","datasets_with_task":"/datasets/task/image-classification"},{"name":"Remote Sensing Image Classification","url":"/task/remote-sensing-image-classification","datasets_with_task":"/datasets/task/remote-sensing-image-classification"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["FireRisk"],"data_loaders":[{"repo":"https://github.com/huggingface/datasets","url":"https://huggingface.co/datasets/blanchon/FireRisk","frameworks":["tf","pytorch","jax"]}],"num_papers_in_archive":3,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/remote-sensing-image-classification-on","task":"Remote Sensing Image Classification","dataset_variant":"FireRisk","rows":4,"metrics":["Accuracy (%)"],"first_row_in_archive_order":{"model":"ResNet-50","paper":"/paper/firerisk-a-remote-sensing-dataset-for-fire","metrics":{"Accuracy (%)":"63.20"},"code_links":[{"title":"charmonyshen/firerisk","url":"https://github.com/charmonyshen/firerisk"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/firerisk-a-remote-sensing-dataset-for-fire","title":"FireRisk: A Remote Sensing Dataset for Fire Risk Assessment with Benchmarks Using Supervised and Self-supervised Learning","date":"2023-03-13","rows_on_this_dataset":4,"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."}