{"url":"/dataset/loveda","name":"LoveDA","full_name":"Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation","description_markdown":"1. 5987 high spatial resolution (0.3 m) remote sensing images from Nanjing, Changzhou, and Wuhan\r\n2. Focus on different geographical environments between Urban and Rural\r\n3. Advance both semantic segmentation and domain adaptation tasks\r\n4. Three considerable challenges:\r\n    * Multi-scale objects\r\n    * Complex background samples\r\n    * Inconsistent class distributions\r\n\r\nTwo contests are held on the Codalab:\r\n[<b>LoveDA Semantic Segmentation Challenge</b>](https://competitions.codalab.org/competitions/35865#), [<b>LoveDA Unsupervised Domain Adaptation Challenge</b>](https://competitions.codalab.org/competitions/35874)","description_withheld":null,"homepage":"http://junjuewang.top/","introduced_date":"2021-10-17","introduced_date_note":null,"introduced_by":{"paper":"/paper/loveda-a-remote-sensing-land-cover-dataset","title":"LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation","first_author":"Junjue Wang","url":null},"license":{"name":"CC BY-NC-SA 4.0","url":"https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","datasets_with_task":"/datasets/task/semantic-segmentation"},{"name":"Domain Adaptation","url":"/task/domain-adaptation","datasets_with_task":"/datasets/task/domain-adaptation"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["LoveDA"],"data_loaders":[{"repo":"https://github.com/WangLibo1995/GeoSeg","url":"https://arxiv.org/ftp/arxiv/papers/2109/2109.08937.pdf","frameworks":["pytorch"]},{"repo":"https://github.com/Junjue-Wang/LoveDA","url":"https://github.com/Junjue-Wang/LoveDA","frameworks":["pytorch"]},{"repo":"https://github.com/Luffy03/DCA","url":"https://github.com/Luffy03/DCA","frameworks":["pytorch"]},{"repo":"https://github.com/Junjue-Wang/LoveNAS","url":"https://github.com/Junjue-Wang/LoveNAS","frameworks":["pytorch"]}],"num_papers_in_archive":81,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/semantic-segmentation-on-loveda","task":"Semantic Segmentation","dataset_variant":"LoveDA","rows":19,"metrics":["Category mIoU"],"first_row_in_archive_order":{"model":"U-Net (MaxViT-S)","paper":"/paper/u-net-ensemble-for-enhanced-semantic","metrics":{"Category mIoU":"56.16"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/dynamic-dictionary-learning-for-remote","title":"Dynamic Dictionary Learning for Remote Sensing Image Segmentation","date":"2025-03-09","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":12,"samples_ran":9,"samples_unverified":3,"pointer_only_for_licence":12,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"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":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/sfa-net-semantic-feature-adjustment-network","title":"SFA-Net: Semantic Feature Adjustment Network for Remote Sensing Image Segmentation","date":"2024-09-03","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/logcan-local-global-class-aware-network-for","title":"LOGCAN++: Adaptive Local-global class-aware network for semantic segmentation of remote sensing imagery","date":"2024-06-24","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/scaling-efficient-masked-autoencoder-learning","title":"Scaling Efficient Masked Image Modeling on Large Remote Sensing Dataset","date":"2024-06-17","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":4,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/u-net-ensemble-for-enhanced-semantic","title":"U-Net Ensemble for Enhanced Semantic Segmentation in Remote Sensing Imagery","date":"2024-06-08","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/mtp-advancing-remote-sensing-foundation-model","title":"MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining","date":"2024-03-20","rows_on_this_dataset":3,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":4,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/aerialformer-multi-resolution-transformer-for","title":"AerialFormer: Multi-resolution Transformer for Aerial Image Segmentation","date":"2023-06-12","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/hi-resnet-a-high-resolution-remote-sensing","title":"Hi-ResNet: Edge Detail Enhancement for High-Resolution Remote Sensing Segmentation","date":"2023-05-22","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/a-billion-scale-foundation-model-for-remote","title":"A Billion-scale Foundation Model for Remote Sensing Images","date":"2023-04-11","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/large-selective-kernel-network-for-remote","title":"Large Selective Kernel Network for Remote Sensing Object Detection","date":"2023-03-16","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/advancing-plain-vision-transformer-towards","title":"Advancing Plain Vision Transformer Towards Remote Sensing Foundation Model","date":"2022-08-08","rows_on_this_dataset":2,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":3,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/loveda-a-remote-sensing-land-cover-dataset","title":"LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation","date":"2021-10-17","rows_on_this_dataset":1,"code_links":5,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":2,"samples_unverified":4,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/efficient-hybrid-transformer-learning-global","title":"UNetFormer: A UNet-like Transformer for Efficient Semantic Segmentation of Remote Sensing Urban Scene Imagery","date":"2021-09-18","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":2,"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":6,"samples_harvested":36,"samples_ran":24,"samples_unverified":12,"pointer_only_for_licence":14,"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."}