{"url":"/dataset/lofar-rfi-detection","name":"LOFAR RFI Detection","full_name":"Low-Frequency Array (LOFAR) Radio Frequency Interference Detection","description_markdown":"This dataset contains simulated and expert-labelled spectrograms from two radio telescopes: the Hydrogen Epoch of Reionization Array (HERA) in South Africa and the Low-Frequency Array (LOFAR) in the Netherlands. These datasets are intended to test radio-frequency interference (RFI) detection schemes. This entry pertains to the LOFAR dataset specifically.\r\n\r\nThe LOFAR dataset is comprised of: \r\n- 7609 Spectrograms and accompanying binary mask information.\r\n\r\nThis dataset has been preprocessed into 512 x 512 x 1 images. The low number of target pixels (RFI) proportional to the total data volume makes this dataset a useful test for RFI detection schemes in radio astronomy and, more generally, anomaly detection or semantic segmentation methods.","description_withheld":null,"homepage":"https://zenodo.org/records/6724065","introduced_date":"2022-06-24","introduced_date_note":null,"introduced_by":{"paper":"/paper/learning-to-detect-rfi-in-radio-astronomy","title":"Learning to detect RFI in radio astronomy without seeing it","first_author":null,"url":null},"license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","datasets_with_task":"/datasets/task/semantic-segmentation"},{"name":"Anomaly Detection","url":"/task/anomaly-detection","datasets_with_task":"/datasets/task/anomaly-detection"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["LOFAR RFI Detection"],"data_loaders":[],"num_papers_in_archive":2,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/semantic-segmentation-on-lofar-rfi-detection","task":"Semantic Segmentation","dataset_variant":"LOFAR RFI Detection","rows":2,"metrics":["AUPRC","AUROC","F1"],"first_row_in_archive_order":{"model":"Nearest Latent Neighbours","paper":"/paper/rfi-detection-with-spiking-neural-networks","metrics":{"AUPRC":"0.414","AUROC":"0.818","F1":"0.48"},"code_links":[{"title":"pritchardn/snn-nln","url":"https://github.com/pritchardn/snn-nln"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/rfi-detection-with-spiking-neural-networks","title":"RFI Detection with Spiking Neural Networks","date":"2023-11-24","rows_on_this_dataset":2,"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."}