{"url":"/dataset/resisc45","name":"RESISC45","full_name":"RESISC45","description_markdown":"RESISC45 dataset is a dataset for Remote Sensing Image Scene Classification (RESISC). It contains 31,500 RGB images of size 256×256 divided into 45 scene classes, each class containing 700 images. Among its notable features, RESISC45 contains varying spatial resolution ranging from 20cm to more than 30m/px.","description_withheld":null,"homepage":"http://www.escience.cn/people/JunweiHan/NWPU-RESISC45.html","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":{"name":"Unspecified","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Image Classification","url":"/task/image-classification","datasets_with_task":"/datasets/task/image-classification"},{"name":"Image Clustering","url":"/task/image-clustering","datasets_with_task":"/datasets/task/image-clustering"},{"name":"Transductive Zero-Shot Classification","url":"/task/transductive-zero-shot-classification","datasets_with_task":"/datasets/task/transductive-zero-shot-classification"},{"name":"Zero-shot Classification (unified classes)","url":"/task/zero-shot-classification-unified-classes","datasets_with_task":"/datasets/task/zero-shot-classification-unified-classes"},{"name":"Scene Classification","url":"/task/scene-classification","datasets_with_task":"/datasets/task/scene-classification"}],"languages":[],"variants":["RESISC45"],"data_loaders":[{"repo":"https://github.com/tensorflow/datasets","url":"https://www.tensorflow.org/datasets/catalog/resisc45","frameworks":["tf","jax"]},{"repo":"https://github.com/microsoft/torchgeo","url":"https://torchgeo.readthedocs.io/en/latest/api/datasets.html#resisc45-remote-sensing-image-scene-classification","frameworks":["pytorch"]},{"repo":"https://github.com/isaaccorley/torchrs","url":"https://github.com/isaaccorley/torchrs","frameworks":["pytorch"]}],"num_papers_in_archive":187,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/image-classification-on-resisc45","task":"Image Classification","dataset_variant":"RESISC45","rows":20,"metrics":["Top 1 Accuracy","F1","zero-shot Acc"],"first_row_in_archive_order":{"model":"ResNet50","paper":"/paper/in-domain-representation-learning-for-remote-1","metrics":{"Top 1 Accuracy":"96.83"},"code_links":[{"title":"google-research/google-research","url":"https://github.com/google-research/google-research/tree/master/remote_sensing_representations"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/image-clustering-on-resisc45","task":"Image Clustering","dataset_variant":"RESISC45","rows":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"TURTLE (CLIP + DINOv2)","paper":"/paper/let-go-of-your-labels-with-unsupervised-1","metrics":{"Accuracy":"89.6"},"code_links":[{"title":"mlbio-epfl/turtle","url":"https://github.com/mlbio-epfl/turtle"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/transductive-zero-shot-classification-on-14","task":"Transductive Zero-Shot Classification","dataset_variant":"RESISC45","rows":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"RS-TransCLIP","paper":"/paper/enhancing-remote-sensing-vision-language","metrics":{"Accuracy":"88"},"code_links":[{"title":"elkhouryk/rs-transclip","url":"https://github.com/elkhouryk/rs-transclip"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/lwganet-a-lightweight-group-attention","title":"LWGANet: A Lightweight Group Attention Backbone for Remote Sensing Visual Tasks","date":"2025-01-17","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"paper":"/paper/sag-vit-a-scale-aware-high-fidelity-patching","title":"SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers","date":"2024-11-14","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/decouplenet-a-lightweight-backbone-network","title":"DecoupleNet: A Lightweight Backbone Network With Efficient Feature Decoupling for Remote Sensing Visual Tasks","date":"2024-09-23","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/enhancing-remote-sensing-vision-language","title":"Enhancing Remote Sensing Vision-Language Models for Zero-Shot Scene Classification","date":"2024-09-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/let-go-of-your-labels-with-unsupervised-1","title":"Let Go of Your Labels with Unsupervised Transfer","date":"2024-06-11","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":3,"samples_unverified":1,"pointer_only_for_licence":4,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/skysense-a-multi-modal-remote-sensing","title":"SkySense: A Multi-Modal Remote Sensing Foundation Model Towards Universal Interpretation for Earth Observation Imagery","date":"2023-12-15","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/all-grains-one-scheme-agos-learning-multi","title":"All Grains, One Scheme (AGOS): Learning Multi-grain Instance Representation for Aerial Scene Classification","date":"2022-05-06","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/vision-models-are-more-robust-and-fair-when","title":"Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision","date":"2022-02-16","rows_on_this_dataset":9,"code_links":1,"syntology":null},{"paper":"/paper/local-semantic-enhanced-convnet-for-aerial","title":"Local semantic enhanced convnet for aerial scene recognition","date":"2021-07-08","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/a-multiple-instance-densely-connected-convnet","title":"A multiple-instance densely-connected ConvNet for aerial scene classification","date":"2020-03-03","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/in-domain-representation-learning-for-remote-1","title":"In-domain representation learning for remote sensing","date":"2019-11-15","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":4,"samples_ran":3,"samples_unverified":1,"pointer_only_for_licence":4,"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."}