{"url":"/dataset/mlrsnet","name":"MLRSNet","full_name":null,"description_markdown":"**MLRSNet** is a a multi-label high spatial resolution remote sensing dataset for semantic scene understanding. It provides different perspectives of the world captured from satellites. That is, it is composed of high spatial resolution optical satellite images. MLRSNet contains 109,161 remote sensing images that are annotated into 46 categories, and the number of sample images in a category varies from 1,500 to 3,000. The images have a fixed size of 256×256 pixels with various pixel resolutions (~10m to 0.1m). Moreover, each image in the dataset is tagged with several of 60 predefined class labels, and the number of labels associated with each image varies from 1 to 13. The dataset can be used for multi-label based image classification, multi-label based image retrieval, and image segmentation.\r\n\r\nSource: [https://github.com/cugbrs/MLRSNet](https://github.com/cugbrs/MLRSNet)\r\nImage Source: [Qi et al](https://arxiv.org/pdf/2010.00243v1.pdf)","description_withheld":null,"homepage":"https://github.com/cugbrs/MLRSNet","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/mlrsnet-a-multi-label-high-spatial-resolution","title":"MLRSNet: A Multi-label High Spatial Resolution Remote Sensing Dataset for Semantic Scene Understanding","first_author":"Xiaoman Qi","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Image Classification","url":"/task/image-classification","datasets_with_task":"/datasets/task/image-classification"},{"name":"Multi-Label Classification","url":"/task/multi-label-classification","datasets_with_task":"/datasets/task/multi-label-classification"},{"name":"Scene Understanding","url":"/task/scene-understanding","datasets_with_task":"/datasets/task/scene-understanding"},{"name":"Transductive Zero-Shot Classification","url":"/task/transductive-zero-shot-classification","datasets_with_task":"/datasets/task/transductive-zero-shot-classification"},{"name":"Scene Classification","url":"/task/scene-classification","datasets_with_task":"/datasets/task/scene-classification"}],"languages":[],"variants":["MLRSNet"],"data_loaders":[{"repo":"https://github.com/cugbrs/MLRSNet","url":"https://github.com/cugbrs/MLRSNet","frameworks":["tf"]}],"num_papers_in_archive":17,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/multi-label-classification-on-mlrsnet","task":"Multi-Label Classification","dataset_variant":"MLRSNet","rows":2,"metrics":["F1-score"],"first_row_in_archive_order":{"model":"ResNet50 (fine-tuning)","paper":"/paper/the-role-of-pre-training-in-high-resolution","metrics":{"F1-score":"92.41"},"code_links":[{"title":"risojevicv/rssc-transfer","url":"https://github.com/risojevicv/rssc-transfer"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/transductive-zero-shot-classification-on-11","task":"Transductive Zero-Shot Classification","dataset_variant":"MLRSNet","rows":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"RS-TransCLIP","paper":"/paper/enhancing-remote-sensing-vision-language","metrics":{"Accuracy":"78.1"},"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/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/the-role-of-pre-training-in-high-resolution","title":"Do we still need ImageNet pre-training in remote sensing scene classification?","date":"2021-11-05","rows_on_this_dataset":2,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-25T09:33:49+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."}