{"url":"/sota/scene-recognition-on-aid","task":{"name":"Scene Recognition","url":"/task/scene-recognition","note":null},"dataset":{"name":"AID","url":"/dataset/aid"},"category":"Computer Vision","categories":["Computer Vision"],"category_note":null,"description":null,"description_from":null,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["Accuracy"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"Accuracy":"higher"}},"counts":{"rows":3,"rows_with_code":3,"rows_with_paper_page":3,"rows_dated":3,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"AGOS","metrics":{"Accuracy":"97.43"},"uses_additional_data":false,"paper_date":"2022-05-06","paper":"/paper/all-grains-one-scheme-agos-learning-multi","paper_url":"https://arxiv.org/abs/2205.03371v1","paper_title":"All Grains, One Scheme (AGOS): Learning Multi-grain Instance Representation for Aerial Scene Classification","code":"https://github.com/biqiwhu/agos","n_code_links":1,"syntology":null},{"rank_in_archive_order":2,"model":"LSENet","metrics":{"Accuracy":"96.36"},"uses_additional_data":false,"paper_date":"2021-07-08","paper":"/paper/local-semantic-enhanced-convnet-for-aerial","paper_url":"https://drive.google.com/file/d/1c1dM43l24mchg8Pcy52mxRaeTg_kfYzY/view","paper_title":"Local semantic enhanced convnet for aerial scene recognition","code":"https://github.com/BiQiWHU/LSENet","n_code_links":1,"syntology":null},{"rank_in_archive_order":3,"model":"MIDC-Net","metrics":{"Accuracy":"92.95"},"uses_additional_data":false,"paper_date":"2020-03-03","paper":"/paper/a-multiple-instance-densely-connected-convnet","paper_url":"https://drive.google.com/file/u/0/d/1a0q-lXSCCrCeoIG_0Cx4cS7ulsg5dLr3/view","paper_title":"A multiple-instance densely-connected ConvNet for aerial scene classification","code":"https://github.com/BiQiWHU/Attention-based-Multi-instance-CNN","n_code_links":1,"syntology":null}],"since_archive":{"present":false,"note":"No Syntology-extracted rows are published in this build."},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":0,"rows_with_any_sample_ran":0,"distinct_papers_with_graph_line":0,"distinct_papers_with_any_sample_ran":0,"samples_over_distinct_papers":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}