{"url":"/dataset/isprs-vaihingen","name":"ISPRS Vaihingen","full_name":"2D Semantic Labeling - Vaihingen data","description_markdown":"The data set contains 33 patches (of different sizes), each consisting of a true orthophoto (TOP) extracted from a larger TOP mosaic.\r\n\r\nSource: [ISPRS](https://www2.isprs.org/commissions/comm2/wg4/benchmark/2d-sem-label-vaihingen/)","description_withheld":null,"homepage":"https://www2.isprs.org/commissions/comm2/wg4/benchmark/2d-sem-label-vaihingen/","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[],"tasks":[{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","datasets_with_task":"/datasets/task/semantic-segmentation"}],"languages":[],"variants":["ISPRS Vaihingen"],"data_loaders":[{"repo":"https://github.com/lironui/Multi-Attention-Network","url":"https://github.com/lironui/Multi-Attention-Network","frameworks":["pytorch"]}],"num_papers_in_archive":19,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/semantic-segmentation-on-isprs-vaihingen","task":"Semantic Segmentation","dataset_variant":"ISPRS Vaihingen","rows":12,"metrics":["Overall Accuracy","Average F1","Category mIoU"],"first_row_in_archive_order":{"model":"LSKNet-S","paper":"/paper/lsknet-a-foundation-lightweight-backbone-for","metrics":{"Average F1":"91.8","Category mIoU":"85.1","Overall Accuracy":"93.6"},"code_links":[{"title":"zcablii/lsknet","url":"https://github.com/zcablii/lsknet"},{"title":"zcablii/Large-Selective-Kernel-Network","url":"https://github.com/zcablii/Large-Selective-Kernel-Network"}]},"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/stochastic-subsampling-with-average-pooling","title":"Stochastic Subsampling With Average Pooling","date":"2024-09-25","rows_on_this_dataset":1,"code_links":0,"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/lsknet-a-foundation-lightweight-backbone-for","title":"LSKNet: A Foundation Lightweight Backbone for Remote Sensing","date":"2024-03-18","rows_on_this_dataset":2,"code_links":2,"syntology":null},{"paper":"/paper/semantic-labeling-of-high-resolution-images","title":"Semantic Labeling of High Resolution Images Using EfficientUNets and Transformers","date":"2022-06-20","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"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":2,"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."}},{"paper":"/paper/transformer-meets-convolution-a-bilateral","title":"Transformer Meets Convolution: A Bilateral Awareness Network for Semantic Segmentation of Very Fine Resolution Urban Scene Images","date":"2021-06-23","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/multiattention-network-for-semantic","title":"Multiattention network for semantic segmentation of fine-resolution remote sensing images","date":"2021-05-15","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/transformer-meets-dcfam-a-novel-semantic","title":"A Novel Transformer Based Semantic Segmentation Scheme for Fine-Resolution Remote Sensing Images","date":"2021-04-25","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."}},{"paper":"/paper/abcnet-attentive-bilateral-contextual-network","title":"ABCNet: Attentive Bilateral Contextual Network for Efficient Semantic Segmentation of Fine-Resolution Remote Sensing Images","date":"2021-02-04","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":3,"samples_harvested":16,"samples_ran":13,"samples_unverified":3,"pointer_only_for_licence":16,"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."}