{"url":"/dataset/mid-air-dataset","name":"Mid-Air Dataset","full_name":null,"description_markdown":"Mid-Air, The Montefiore Institute Dataset of Aerial Images and Records, is a multi-purpose synthetic dataset for low altitude drone flights. It provides a large amount of synchronized data corresponding to flight records for multi-modal vision sensors and navigation sensors mounted on board of a flying quadcopter. Multi-modal vision sensors capture RGB pictures, relative surface normal orientation, depth, object semantics and stereo disparity.\r\n\r\nSource: [Mid-Air Dataset](https://midair.ulg.ac.be)","description_withheld":null,"homepage":"https://midair.ulg.ac.be","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":{"name":"CC BY-NC-SA 4.0","url":"https://creativecommons.org/licenses/by-nc-sa/4.0/"},"modalities":[],"tasks":[{"name":"Monocular Depth Estimation","url":"/task/monocular-depth-estimation","datasets_with_task":"/datasets/task/monocular-depth-estimation"},{"name":"Depth Aleatoric Uncertainty Estimation","url":"/task/depth-aleatoric-uncertainty-estimation","datasets_with_task":"/datasets/task/depth-aleatoric-uncertainty-estimation"}],"languages":[],"variants":["Mid-Air Dataset"],"data_loaders":[],"num_papers_in_archive":6,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/monocular-depth-estimation-on-mid-air-dataset","task":"Monocular Depth Estimation","dataset_variant":"Mid-Air Dataset","rows":6,"metrics":["Abs Rel","RMSE","RMSE log","SQ Rel"],"first_row_in_archive_order":{"model":"M4Depth+U","paper":"/paper/a-technique-to-jointly-estimate-depth-and","metrics":{"Abs Rel":"0.134","RMSE log":"0.188"},"code_links":[{"title":"michael-fonder/m4depthu","url":"https://github.com/michael-fonder/m4depthu"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/depth-aleatoric-uncertainty-estimation-on-mid","task":"Depth Aleatoric Uncertainty Estimation","dataset_variant":"Mid-Air Dataset","rows":1,"metrics":["AuSE on Abs Rel","AuSE on RMSE log"],"first_row_in_archive_order":{"model":"M4Depth+U","paper":"/paper/a-technique-to-jointly-estimate-depth-and","metrics":{"AuSE on Abs Rel":"0.007","AuSE on RMSE log":"0.02"},"code_links":[{"title":"michael-fonder/m4depthu","url":"https://github.com/michael-fonder/m4depthu"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/a-technique-to-jointly-estimate-depth-and","title":"A technique to jointly estimate depth and depth uncertainty for unmanned aerial vehicles","date":"2023-05-31","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/m4depth-a-motion-based-approach-for-monocular","title":"M4Depth: Monocular depth estimation for autonomous vehicles in unseen environments","date":"2021-05-20","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/exploiting-temporal-consistency-for-real-time","title":"Exploiting temporal consistency for real-time video depth estimation","date":"2019-08-10","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":20,"samples_ran":3,"samples_unverified":17,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/recurrent-neural-network-for-un-supervised-1","title":"Recurrent Neural Network for (Un-)Supervised Learning of Monocular Video Visual Odometry and Depth","date":"2019-06-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/digging-into-self-supervised-monocular-depth","title":"Digging Into Self-Supervised Monocular Depth Estimation","date":"2018-06-04","rows_on_this_dataset":1,"code_links":15,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":24,"samples_ran":17,"samples_unverified":7,"pointer_only_for_licence":6,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/unsupervised-monocular-depth-estimation-with","title":"Unsupervised Monocular Depth Estimation with Left-Right Consistency","date":"2016-09-13","rows_on_this_dataset":1,"code_links":16,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":12,"samples_ran":3,"samples_unverified":9,"pointer_only_for_licence":5,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":3,"samples_harvested":56,"samples_ran":23,"samples_unverified":33,"pointer_only_for_licence":11,"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."}