{"url":"/dataset/sensaturban","name":"SensatUrban","full_name":null,"description_markdown":"The SensatUrbat dataset is an urban-scale photogrammetric point cloud dataset with nearly three billion richly annotated points, which is five times the number of labeled points than the existing largest point cloud dataset. The dataset consists of large areas from two UK cities, covering about 6 km^2 of the city landscape. In the dataset, each 3D point is labeled as one of 13 semantic classes, such as ground, vegetation, car, etc..\r\n\r\nSource: [https://github.com/QingyongHu/SensatUrban](https://github.com/QingyongHu/SensatUrban)\nImage Source: [https://github.com/QingyongHu/SensatUrban](https://github.com/QingyongHu/SensatUrban)","description_withheld":null,"homepage":"https://github.com/QingyongHu/SensatUrban","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/towards-semantic-segmentation-of-urban-scale","title":"Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges","first_author":"Qingyong Hu","url":null},"license":null,"modalities":[{"name":"Point cloud","url":"/datasets/modality/point-cloud"}],"tasks":[{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","datasets_with_task":"/datasets/task/semantic-segmentation"},{"name":"Scene Understanding","url":"/task/scene-understanding","datasets_with_task":"/datasets/task/scene-understanding"},{"name":"3D Semantic Segmentation","url":"/task/3d-semantic-segmentation","datasets_with_task":"/datasets/task/3d-semantic-segmentation"}],"languages":[],"variants":["SensatUrban"],"data_loaders":[{"repo":"https://github.com/QingyongHu/SensatUrban","url":"https://github.com/QingyongHu/SensatUrban","frameworks":[]}],"num_papers_in_archive":28,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/3d-semantic-segmentation-on-sensaturban","task":"3D Semantic Segmentation","dataset_variant":"SensatUrban","rows":8,"metrics":["mIoU","oAcc"],"first_row_in_archive_order":{"model":"LCPFormer","paper":"/paper/lcpformer-towards-effective-3d-point-cloud","metrics":{"mIoU":"63.4"},"code_links":[{"title":"zhh6425/LocalContextPropagation","url":"https://github.com/zhh6425/LocalContextPropagation"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/mrnet-multiple-input-receptive-field-network","title":"Human Vision Based 3D Point Cloud Semantic Segmentation of Large-Scale Outdoor Scene","date":"2023-01-30","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/lcpformer-towards-effective-3d-point-cloud","title":"LCPFormer: Towards Effective 3D Point Cloud Analysis via Local Context Propagation in Transformers","date":"2022-10-23","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/efficient-urban-scale-point-clouds","title":"Efficient Urban-scale Point Clouds Segmentation with BEV Projection","date":"2021-09-19","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/scf-net-learning-spatial-contextual-features","title":"SCF-Net: Learning Spatial Contextual Features for Large-Scale Point Cloud Segmentation","date":"2021-06-19","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/kpconv-flexible-and-deformable-convolution","title":"KPConv: Flexible and Deformable Convolution for Point Clouds","date":"2019-04-18","rows_on_this_dataset":1,"code_links":10,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":12,"samples_ran":5,"samples_unverified":7,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/tangent-convolutions-for-dense-prediction-in","title":"Tangent Convolutions for Dense Prediction in 3D","date":"2018-07-06","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/3d-semantic-segmentation-with-submanifold","title":"3D Semantic Segmentation with Submanifold Sparse Convolutional Networks","date":"2017-11-28","rows_on_this_dataset":1,"code_links":6,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/large-scale-point-cloud-semantic-segmentation","title":"Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs","date":"2017-11-27","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":15,"samples_ran":8,"samples_unverified":7,"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."}