{"url":"/dataset/dales","name":"DALES","full_name":"DALES: A Large-scale Aerial LiDAR Data Set for Semantic Segmentation","description_markdown":"We present the Dayton Annotated LiDAR Earth Scan (DALES) data set, a new large-scale aerial LiDAR data set with over a half-billion hand-labeled points spanning 10 square kilometers of area and eight object categories. Large annotated point cloud data sets have become the standard for evaluating deep learning methods. However, most of the existing data sets focus on data collected from a mobile or terrestrial scanner with few focusing on aerial data. Point cloud data collected from an Aerial Laser Scanner (ALS) presents a new set of challenges and applications in areas such as 3D urban modeling and large-scale surveillance. DALES is the most extensive publicly available ALS data set with over 400 times the number of points and six times the resolution of other currently available annotated aerial point cloud data sets. This data set gives a critical number of expert verified hand-labeled points for the evaluation of new 3D deep learning algorithms, helping to expand the focus of current algorithms to aerial data. We describe the nature of our data, annotation workflow, and provide a benchmark of current state-of-the-art algorithm performance on the DALES data set.","description_withheld":null,"homepage":"https://udayton.edu/engineering/research/centers/vision_lab/research/was_data_analysis_and_processing/dale.php","introduced_date":"2020-04-14","introduced_date_note":null,"introduced_by":{"paper":"/paper/dales-a-large-scale-aerial-lidar-data-set-for","title":"DALES: A Large-scale Aerial LiDAR Data Set for Semantic Segmentation","first_author":"Nina Varney","url":null},"license":{"name":"Creative Commons 3.0","url":"https://creativecommons.org/licenses/by/3.0/legalcode"},"modalities":[{"name":"3D","url":"/datasets/modality/3d"},{"name":"Point cloud","url":"/datasets/modality/point-cloud"},{"name":"LiDAR","url":"/datasets/modality/lidar"}],"tasks":[{"name":"Panoptic Segmentation","url":"/task/panoptic-segmentation","datasets_with_task":"/datasets/task/panoptic-segmentation"},{"name":"3D Semantic Segmentation","url":"/task/3d-semantic-segmentation","datasets_with_task":"/datasets/task/3d-semantic-segmentation"}],"languages":[],"variants":["DALES"],"data_loaders":[],"num_papers_in_archive":26,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/3d-semantic-segmentation-on-dales","task":"3D Semantic Segmentation","dataset_variant":"DALES","rows":9,"metrics":["mIoU","Overall Accuracy","Model size"],"first_row_in_archive_order":{"model":"KPConv","paper":"/paper/kpconv-flexible-and-deformable-convolution","metrics":{"Model size":"14M","Overall Accuracy":"97.8","mIoU":"81.1"},"code_links":[{"title":"isl-org/Open3D-ML","url":"https://github.com/isl-org/Open3D-ML"},{"title":"HuguesTHOMAS/KPConv-PyTorch","url":"https://github.com/HuguesTHOMAS/KPConv-PyTorch"},{"title":"HuguesTHOMAS/KPConv","url":"https://github.com/HuguesTHOMAS/KPConv"},{"title":"ldkong1205/Robo3D","url":"https://github.com/ldkong1205/Robo3D"},{"title":"XuyangBai/KPConv.pytorch","url":"https://github.com/XuyangBai/KPConv.pytorch"},{"title":"plusmultiply/mprm","url":"https://github.com/plusmultiply/mprm"},{"title":"Arjun-NA/KPConv_for_DALES","url":"https://github.com/Arjun-NA/KPConv_for_DALES"},{"title":"Yacovitch/EyeNet","url":"https://github.com/Yacovitch/EyeNet"},{"title":"genglinliu/KPConv_Pytorch","url":"https://github.com/genglinliu/KPConv_Pytorch"},{"title":"JohnRomanelis/KPConv_torch_geometric","url":"https://github.com/JohnRomanelis/KPConv_torch_geometric"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/panoptic-segmentation-on-dales","task":"Panoptic Segmentation","dataset_variant":"DALES","rows":1,"metrics":["PQ","RQ","SQ","Params (M)"],"first_row_in_archive_order":{"model":"SuperCluster","paper":"/paper/scalable-3d-panoptic-segmentation-with","metrics":{"PQ":"61.2","Params (M)":"0.21","RQ":"68.6","SQ":"87.1"},"code_links":[{"title":"drprojects/superpoint_transformer","url":"https://github.com/drprojects/superpoint_transformer"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/scalable-3d-panoptic-segmentation-with","title":"Scalable 3D Panoptic Segmentation As Superpoint Graph Clustering","date":"2024-01-12","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/efficient-3d-semantic-segmentation-with-1","title":"Efficient 3D Semantic Segmentation with Superpoint Transformer","date":"2023-06-13","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"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/shellnet-efficient-point-cloud-convolutional","title":"ShellNet: Efficient Point Cloud Convolutional Neural Networks using Concentric Shells Statistics","date":"2019-08-17","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":"2026-09-25T09:33:49+00:00","samples_harvested":12,"samples_ran":8,"samples_unverified":4,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/generalizing-discrete-convolutions-for","title":"ConvPoint: Continuous Convolutions for Point Cloud Processing","date":"2019-04-04","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":4,"samples_ran":4,"samples_unverified":0,"pointer_only_for_licence":4,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/pointcnn-convolution-on-x-transformed-points","title":"PointCNN: Convolution On X-Transformed Points","date":"2018-12-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"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},{"paper":"/paper/pointnet-deep-hierarchical-feature-learning","title":"PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space","date":"2017-06-07","rows_on_this_dataset":1,"code_links":68,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":67,"samples_ran":36,"samples_unverified":31,"pointer_only_for_licence":26,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":3,"samples_harvested":83,"samples_ran":48,"samples_unverified":35,"pointer_only_for_licence":33,"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."}