{"url":"/dataset/whu-building-dataset","name":"WHU Building Dataset","full_name":null,"description_markdown":"We manually edited an aerial and a satellite imagery dataset of building samples and named it a WHU building dataset. The aerial dataset consists of more than 220, 000 independent buildings extracted from aerial images with 0.075 m spatial resolution and 450 km2 covering in Christchurch, New Zealand. The satellite imagery dataset consists of two subsets. One of them is collected from cities over the world and from various remote sensing resources including QuickBird, Worldview series, IKONOS, ZY-3, etc. The other satellite building sub-dataset consists of 6 neighboring satellite images covering 550 km2 on East Asia with 2.7 m ground resolution.","description_withheld":null,"homepage":"http://gpcv.whu.edu.cn/data/building_dataset.html","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[],"tasks":[{"name":"Change Detection","url":"/task/change-detection","datasets_with_task":"/datasets/task/change-detection"},{"name":"Extracting Buildings In Remote Sensing Images","url":"/task/extracting-buildings-in-remote-sensing-images","datasets_with_task":"/datasets/task/extracting-buildings-in-remote-sensing-images"},{"name":"Building change detection for remote sensing images","url":"/task/building-change-detection-for-remote-sensing","datasets_with_task":"/datasets/task/building-change-detection-for-remote-sensing"}],"languages":[],"variants":["WHU Building Dataset"],"data_loaders":[{"repo":"https://github.com/WangZhenqing-RS/MEC-Net","url":"https://github.com/WangZhenqing-RS/MEC-Net/blob/main/code/train.py","frameworks":["pytorch"]}],"num_papers_in_archive":17,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/change-detection-on-whu-building-dataset","task":"Change Detection","dataset_variant":"WHU Building Dataset","rows":7,"metrics":["F1-score"],"first_row_in_archive_order":{"model":"IMP+MTP(InternImage-XL)","paper":"/paper/mtp-advancing-remote-sensing-foundation-model","metrics":{"F1-score":"0.9559"},"code_links":[{"title":"vitae-transformer/mtp","url":"https://github.com/vitae-transformer/mtp"},{"title":"cuzyoung/crossearth","url":"https://github.com/cuzyoung/crossearth"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/extracting-buildings-in-remote-sensing-images-2","task":"Extracting Buildings In Remote Sensing Images","dataset_variant":"WHU Building Dataset","rows":7,"metrics":["F1","IoU"],"first_row_in_archive_order":{"model":"SiamixFormer-5","paper":"/paper/siamixformer-a-siamese-transformer-network","metrics":{"F1":"96.69","IoU":"93.58"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/building-change-detection-for-remote-sensing-1","task":"Building change detection for remote sensing images","dataset_variant":"WHU Building Dataset","rows":2,"metrics":["F1","IoU","Params(M)"],"first_row_in_archive_order":{"model":"SRC-Net","paper":"/paper/src-net-bi-temporal-spatial-relationship","metrics":{"F1":"92.06","IoU":"85.28","Params(M)":"5.17"},"code_links":[{"title":"Chnja/SRCNet","url":"https://github.com/Chnja/SRCNet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/src-net-bi-temporal-spatial-relationship","title":"SRC-Net: Bi-Temporal Spatial Relationship Concerned Network for Change Detection","date":"2024-06-09","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/rs-mamba-for-large-remote-sensing-image-dense","title":"RS-Mamba for Large Remote Sensing Image Dense Prediction","date":"2024-04-03","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":2,"samples_unverified":4,"pointer_only_for_licence":6,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"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/exchanging-dual-encoder-decoder-a-new","title":"Exchanging Dual Encoder-Decoder: A New Strategy for Change Detection with Semantic Guidance and Spatial Localization","date":"2023-11-19","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"paper":"/paper/building-extraction-from-remote-sensing-1","title":"Building Extraction from Remote Sensing Images via an Uncertainty-Aware Network","date":"2023-07-23","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/siamixformer-a-siamese-transformer-network","title":"SiamixFormer: a fully-transformer Siamese network with temporal Fusion for accurate building detection and change detection in bi-temporal remote sensing images","date":"2022-08-01","rows_on_this_dataset":6,"code_links":0,"syntology":null},{"paper":"/paper/tinycd-a-not-so-deep-learning-model-for","title":"TINYCD: A (Not So) Deep Learning Model For Change Detection","date":"2022-07-26","rows_on_this_dataset":1,"code_links":2,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":2,"samples_harvested":11,"samples_ran":6,"samples_unverified":5,"pointer_only_for_licence":6,"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."}