{"url":"/dataset/rsscn7","name":"RSSCN7","full_name":null,"description_markdown":"he RSSCN7 dataset contains satellite images acquired from Google Earth, which is originally collected for remote sensing scene classification. We conduct image synthesis on RSSCN7 to make it capable of the image inpainting task. It has seven classes: grassland, farmland, industrial and commercial regions, river and lake, forest field, residential region, and parking lot. Each class has 400 images, so there are total 2,800 images in the RSSCN7 dataset.","description_withheld":null,"homepage":"https://github.com/palewithout/RSSCN7","introduced_date":"2017-03-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/remote-sensing-image-scene-classification","title":"Remote Sensing Image Scene Classification: Benchmark and State of the Art","first_author":"Gong Cheng","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Classification","url":"/task/classification-1","datasets_with_task":"/datasets/task/classification-1"}],"languages":[],"variants":["RSSCN7"],"data_loaders":[],"num_papers_in_archive":20,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/classification-on-rsscn7","task":"Classification","dataset_variant":"RSSCN7","rows":2,"metrics":["1:1 Accuracy"],"first_row_in_archive_order":{"model":"GLNet","paper":"/paper/convolutional-neural-networks-based-remote","metrics":{"1:1 Accuracy":"95.07"},"code_links":[{"title":"wuchangsheng951/GLNET","url":"https://github.com/wuchangsheng951/GLNET"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/convolutional-neural-networks-based-remote","title":"Convolutional Neural Networks Based Remote Sensing Scene Classification under Clear and Cloudy Environments","date":"2021-12-30","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/convolutional-pose-machines","title":"Convolutional Pose Machines","date":"2016-01-30","rows_on_this_dataset":1,"code_links":50,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":4,"samples_ran":1,"samples_unverified":3,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":1,"samples_harvested":4,"samples_ran":1,"samples_unverified":3,"pointer_only_for_licence":3,"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."}