{"url":"/dataset/flame","name":"FLAME","full_name":"Fire Luminosity Airborne-based Machine learning Evaluation","description_markdown":"FLAME is a fire image dataset collected by drones during a prescribed burning piled detritus in an Arizona pine forest. The dataset includes video recordings and thermal heatmaps captured by infrared cameras. The captured videos and images are annotated and labeled frame-wise to help researchers easily apply their fire detection and modeling algorithms.\r\n\r\nSource: [Aerial Imagery Pile burn detection using Deep Learning: the FLAME dataset](/paper/aerial-imagery-pile-burn-detection-using-deep)","description_withheld":null,"homepage":"https://github.com/AlirezaShamsoshoara/Fire-Detection-UAV-Aerial-Image-Classification-Segmentation-UnmannedAerialVehicle","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/aerial-imagery-pile-burn-detection-using-deep","title":"Aerial Imagery Pile burn detection using Deep Learning: the FLAME dataset","first_author":"Alireza Shamsoshoara","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Real-Time Semantic Segmentation","url":"/task/real-time-semantic-segmentation","datasets_with_task":"/datasets/task/real-time-semantic-segmentation"}],"languages":[],"variants":["FLAME"],"data_loaders":[{"repo":"https://github.com/AlirezaShamsoshoara/Fire-Detection-UAV-Aerial-Image-Classification-Segmentation-UnmannedAerialVehicle","url":"https://github.com/AlirezaShamsoshoara/Fire-Detection-UAV-Aerial-Image-Classification-Segmentation-UnmannedAerialVehicle","frameworks":["tf"]}],"num_papers_in_archive":12,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/real-time-semantic-segmentation-on-flame","task":"Real-Time Semantic Segmentation","dataset_variant":"FLAME","rows":1,"metrics":["FPS","Mean Intersection over Union","Mean Pixel Accuracy"],"first_row_in_archive_order":{"model":"Fast DeepLabV3+","paper":"/paper/a-real-time-fire-segmentation-method-based-on","metrics":{"FPS":"59","Mean Intersection over Union":"86.98","Mean Pixel Accuracy":"92.46"},"code_links":[{"title":"maidacundo/real-time-fire-segmentation-deep-learning","url":"https://github.com/maidacundo/real-time-fire-segmentation-deep-learning"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/a-real-time-fire-segmentation-method-based-on","title":"A Real-time Fire Segmentation Method Based on A Deep Learning Approach","date":"2022-07-22","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."}