{"url":"/dataset/deep-indices","name":"Deep Indices","full_name":"multi-spectral leaf/vegetation segmentation","description_markdown":"This dataset inclue multi-spectral acquisition of vegetation for the conception of new DeepIndices. The images were acquired with the Airphen (Hyphen, Avignon, France) six-band multi-spectral camera configured using the 450/570/675/710/730/850 nm bands with a 10 nm FWHM. The dataset were acquired on the site of INRAe in Montoldre (Allier, France, at 46°20'30.3\"N 3°26'03.6\"E) within the framework of the “RoSE challenge” founded by the French National Research Agency (ANR) and in Dijon (Burgundy, France, at 47°18'32.5\"N 5°04'01.8\"E) within the site of AgroSup Dijon. Images of bean and corn, containing various natural weeds (yarrows, amaranth, geranium, plantago, etc) and sowed ones (mustards, goosefoots, mayweed and ryegrass) with very distinct characteristics in terms of illumination (shadow, morning, evening, full sun, cloudy, rain, ...) were acquired in top-down view at 1.8 meter from the ground. (2020-05-01)","description_withheld":null,"homepage":"https://data.inrae.fr/dataset.xhtml?persistentId=doi:10.15454/DSQC8N","introduced_date":"2021-05-26","introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Environment","url":"/datasets/modality/environment"},{"name":"Hyperspectral images","url":"/datasets/modality/hyperspectral-images"},{"name":"RGB-D","url":"/datasets/modality/rgb-d"}],"tasks":[{"name":"2D Semantic Segmentation","url":"/task/2d-semantic-segmentation","datasets_with_task":"/datasets/task/2d-semantic-segmentation"}],"languages":[{"name":"English","url":"/datasets/language/english"},{"name":"French","url":"/datasets/language/french"}],"variants":["Deep Indices"],"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/2d-semantic-segmentation-on-deep-indices","task":"2D Semantic Segmentation","dataset_variant":"Deep Indices","rows":23,"metrics":["mIoU"],"first_row_in_archive_order":{"model":"7x7 dense-morphological + ibf + sprb","paper":"/paper/deepindices-remote-sensing-indices-based-on","metrics":{"mIoU":"82.19"},"code_links":[{"title":"phd-thesis-adventice/phd-index-optimizer-tensorflow","url":"https://gitlab.com/phd-thesis-adventice/phd-index-optimizer-tensorflow"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/deepindices-remote-sensing-indices-based-on","title":"DeepIndices: Remote Sensing Indices Based on Approximation of Functions through Deep-Learning, Application to Uncalibrated Vegetation Images","date":"2021-06-11","rows_on_this_dataset":23,"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."}