{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T23:02:31Z","timestamp":1776294151234,"version":"3.50.1"},"reference-count":50,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2024,4,18]],"date-time":"2024-04-18T00:00:00Z","timestamp":1713398400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"State Key Project of the National Natural Science Foundation of China\u2013Key projects of the joint fund for regional innovation and development","award":["U22A20566"],"award-info":[{"award-number":["U22A20566"]}]},{"name":"State Key Project of the National Natural Science Foundation of China\u2013Key projects of the joint fund for regional innovation and development","award":["42271365"],"award-info":[{"award-number":["42271365"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U22A20566"],"award-info":[{"award-number":["U22A20566"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42271365"],"award-info":[{"award-number":["42271365"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The complexity of terrain features poses a substantial challenge in the effective processing and application of airborne LiDAR data, particularly in regions characterized by steep slopes and diverse objects. In this paper, we propose a novel multiscale filtering method utilizing a modified 3D alpha shape algorithm to increase the ground point extraction accuracy in complex terrain. Our methodology comprises three pivotal stages: preprocessing for outlier removal and potential ground point extraction; the deployment of a modified 3D alpha shape to construct multiscale point cloud layers; and the use of a multiscale triangulated irregular network (TIN) densification process for precise ground point extraction. In each layer, the threshold is adaptively determined based on the corresponding \u03b1. Points closer to the TIN surface than the threshold are identified as ground points. The performance of the proposed method was validated using a classical benchmark dataset provided by the ISPRS and an ultra-large-scale ground filtering dataset called OpenGF. The experimental results demonstrate that this method is effective, with an average total error and a kappa coefficient on the ISPRS dataset of 3.27% and 88.97%, respectively. When tested in the large scenarios of the OpenGF dataset, the proposed method outperformed four classical filtering methods and achieved accuracy comparable to that of the best of learning-based methods.<\/jats:p>","DOI":"10.3390\/rs16081443","type":"journal-article","created":{"date-parts":[[2024,4,18]],"date-time":"2024-04-18T10:30:52Z","timestamp":1713436252000},"page":"1443","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A Multiscale Filtering Method for Airborne LiDAR Data Using Modified 3D Alpha Shape"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-3217-9389","authenticated-orcid":false,"given":"Di","family":"Cao","sequence":"first","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-2257-9110","authenticated-orcid":false,"given":"Cheng","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-1722-5558","authenticated-orcid":false,"given":"Meng","family":"Du","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Xiaohuan","family":"Xi","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/S0924-2716(98)00009-4","article-title":"Determination of terrain models in wooded areas with airborne laser scanner data","volume":"53","author":"Kraus","year":"1998","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_2","first-page":"110","article-title":"DEM Generation from Laser Scanner Data Using adaptive TIN Models","volume":"23","author":"Axelsson","year":"2000","journal-title":"Int. Arch. Photogramm. Remote Sens."},{"key":"ref_3","first-page":"628","article-title":"DEM gemeration from airborne lidar data by an adaptive dualdirectional slope filter","volume":"38","author":"Wang","year":"2010","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci.-Arch."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1804","DOI":"10.3390\/rs4061804","article-title":"Adaptive Slope Filtering of Airborne LiDAR Data in Urban Areas for Digital Terrain Model (DTM) Generation","volume":"4","author":"Susaki","year":"2012","journal-title":"Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2011.10.002","article-title":"Parameter-free ground filtering of LiDAR data for automatic DTM generation","volume":"67","author":"Mongus","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhang, J., Hu, X., Hengming, D., and Qu, S. (2020). DEM Extraction from ALS Point Clouds in Forest Areas via Graph Convolution Network. Remote Sens., 12.","DOI":"10.3390\/rs12010178"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Krisanski, S., Taskhiri, M.S., Gonzalez Aracil, S., Herries, D., Muneri, A., Gurung, M.B., Montgomery, J., and Turner, P. (2021). Forest Structural Complexity Tool\u2014An Open Source, Fully-Automated Tool for Measuring Forest Point Clouds. Remote Sens., 13.","DOI":"10.3390\/rs13224677"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3533","DOI":"10.1109\/JSTARS.2022.3171771","article-title":"A Crown Guess and Selection Framework for Individual Tree Detection From ALS Point Clouds","volume":"15","author":"Wang","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Chen, S., Wang, C., Dai, H., Zhang, H., Pan, F., Xi, X., Yan, Y., Wang, P., Yang, X., and Zhu, X. (2019). Power Pylon Reconstruction Based on Abstract Template Structures Using Airborne LiDAR Data. Remote Sens., 11.","DOI":"10.3390\/rs11131579"},{"key":"ref_10","first-page":"103263","article-title":"An automatic framework for pylon detection by a hierarchical coarse-to-fine segmentation of powerline corridors from UAV LiDAR point clouds","volume":"118","author":"Shen","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.isprsjprs.2013.12.002","article-title":"Ground and building extraction from LiDAR data based on differential morphological profiles and locally fitted surfaces","volume":"93","author":"Mongus","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wang, Q., Yan, L., Zhang, L., Ai, H., and Lin, X. (2016). A Semantic Modelling Framework-Based Method for Building Reconstruction from Point Clouds. Remote Sens., 8.","DOI":"10.3390\/rs8090737"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.isprsjprs.2014.02.014","article-title":"An adaptive surface filter for airborne laser scanning point clouds by means of regularization and bending energy","volume":"92","author":"Hu","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"833","DOI":"10.3390\/rs2030833","article-title":"Ground Filtering Algorithms for Airborne LiDAR Data: A Review of Critical Issues","volume":"2","author":"Meng","year":"2010","journal-title":"Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.isprsjprs.2004.05.004","article-title":"Experimental comparison of filter algorithms for bare-Earth extraction from airborne laser scanning point clouds","volume":"59","author":"Sithole","year":"2004","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","first-page":"678","article-title":"Slope based filtering of laser altimetry data","volume":"33","author":"Vosselman","year":"2000","journal-title":"Int. Arch. Photogramm. Remote Sens."},{"key":"ref_17","first-page":"203","article-title":"Filtering of laser altimetry data using a slope adaptive filter","volume":"34","author":"Sithole","year":"2001","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1177\/0309133308089496","article-title":"Airborne LiDAR for DEM generation: Some critical issues","volume":"32","author":"Liu","year":"2008","journal-title":"Prog. Phys. Geogr. Earth Environ."},{"key":"ref_19","first-page":"W19","article-title":"Filtering of airborne laser scanner data based on segmented point clouds","volume":"36","author":"Sithole","year":"2005","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"872","DOI":"10.1109\/TGRS.2003.810682","article-title":"A progressive morphological filter for removing nonground measurements from airborne LIDAR data","volume":"41","author":"Zhang","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"175","DOI":"10.14358\/PERS.73.2.175","article-title":"Filtering Airborne Laser Scanning Data with Morphological Methods","volume":"73","author":"Chen","year":"2007","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1016\/j.optlastec.2013.06.007","article-title":"A gradient-constrained morphological filtering algorithm for airborne LiDAR","volume":"54","author":"Li","year":"2013","journal-title":"Opt. Laser Technol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"133","DOI":"10.14358\/PERS.80.2.133-141","article-title":"Filtering Airborne Lidar Data by Modified White Top-Hat Transform with Directional Edge Constraints","volume":"80","author":"Li","year":"2014","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.isprsjprs.2012.12.002","article-title":"An improved simple morphological filter for the terrain classification of airborne LIDAR data","volume":"77","author":"Pingel","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Hui, Z., Hu, Y., Yevenyo, Y.Z., and Yu, X. (2016). An Improved Morphological Algorithm for Filtering Airborne LiDAR Point Cloud Based on Multi-Level Kriging Interpolation. Remote Sens., 8.","DOI":"10.3390\/rs8010035"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1109\/JSTARS.2013.2262996","article-title":"Computationally Efficient Method for the Generation of a Digital Terrain Model From Airborne LiDAR Data Using Connected Operators","volume":"7","author":"Mongus","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Chen, Z., Gao, B., and Devereux, B. (2017). State-of-the-Art: DTM Generation Using Airborne LIDAR Data. Sensors, 17.","DOI":"10.3390\/s17010150"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.isprsjprs.2016.03.016","article-title":"Improved progressive TIN densification filtering algorithm for airborne LiDAR data in forested areas","volume":"117","author":"Zhao","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.measurement.2017.03.007","article-title":"A revised progressive TIN densification for filtering airborne LiDAR data","volume":"104","author":"Nie","year":"2017","journal-title":"Measurement"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.isprsjprs.2013.04.001","article-title":"Filtering airborne LiDAR data by embedding smoothness-constrained segmentation in progressive TIN densification","volume":"81","author":"Zhang","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhang, W., Qi, J., Wan, P., Wang, H., Xie, D., Wang, X., and Yan, G. (2016). An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation. Remote Sens., 8.","DOI":"10.3390\/rs8060501"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2013.05.001","article-title":"A multiresolution hierarchical classification algorithm for filtering airborne LiDAR data","volume":"82","author":"Chen","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3958","DOI":"10.1109\/JSTARS.2020.3008477","article-title":"A Point-Based Fully Convolutional Neural Network for Airborne LiDAR Ground Point Filtering in Forested Environments","volume":"13","author":"Jin","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.isprsjprs.2023.06.005","article-title":"Deep learning for filtering the ground from ALS point clouds: A dataset, evaluations and issues","volume":"202","author":"Qin","year":"2023","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_35","unstructured":"Qi, C.R., Yi, L., Su, H., and Guibas, L.J. (2017). PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Thomas, H., Qi, C.R., Deschaud, J.E., Marcotegui, B., Goulette, F., and Guibas, L. (November, January 27). KPConv: Flexible and Deformable Convolution for Point Clouds. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.00651"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Hu, Q., Yang, B., Xie, L., Rosa, S., Guo, Y., Wang, Z., Trigoni, N., and Markham, A. (2020, January 13\u201319). RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01112"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Fan, S., Dong, Q., Zhu, F., Lv, Y., Ye, P., and Wang, F.Y. (2021, January 20\u201325). SCF-Net: Learning Spatial Contextual Features for Large-Scale Point Cloud Segmentation. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01427"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1093\/bib\/bbs077","article-title":"Alpha shape and Delaunay triangulation in studies of protein-related interactions","volume":"15","author":"Zhou","year":"2014","journal-title":"Brief. Bioinform."},{"key":"ref_40","first-page":"103728","article-title":"Accurate and complete line segment extraction for large-scale point clouds","volume":"128","author":"Xin","year":"2024","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Tian, P., Hua, X., Tao, W., and Zhang, M. (2022). Robust Extraction of 3D Line Segment Features from Unorganized Building Point Clouds. Remote Sens., 14.","DOI":"10.3390\/rs14143279"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"103592","DOI":"10.1016\/j.cad.2023.103592","article-title":"Robust and Accurate Feature Detection on Point Clouds","volume":"164","author":"Liu","year":"2023","journal-title":"Comput.-Aided Des."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Ma, W., and Li, Q. (2019). An Improved Ball Pivot Algorithm-Based Ground Filtering Mechanism for LiDAR Data. Remote Sens., 11.","DOI":"10.3390\/rs11101179"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Qin, N., Tan, W., Ma, L., Zhang, D., and Li, J. (2021, January 19\u201325). OpenGF: An Ultra-Large-Scale Ground Filtering Dataset Built Upon Open ALS Point Clouds Around the World. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021, Nashville, TN, USA.","DOI":"10.1109\/CVPRW53098.2021.00119"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1109\/TIT.1983.1056714","article-title":"On the shape of a set of points in the plane","volume":"29","author":"Edelsbrunner","year":"1983","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1145\/174462.156635","article-title":"Three-dimensional alpha shapes","volume":"13","author":"Edelsbrunner","year":"1994","journal-title":"ACM Trans. Graph."},{"key":"ref_47","unstructured":"Bernardini, F., and Bajaj, C. (1997). Sampling and Reconstructing Manifolds Using Alpha-Shapes, Purdue University. Technical Report CSD-TR-97-013."},{"key":"ref_48","unstructured":"The CGAL Project (2024, April 01). Computational Geometry Algorithms Library (CGAL). Available online: https:\/\/doc.cgal.org\/latest\/Manual\/packages.html."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0034-4257(91)90048-B","article-title":"A review of assessing the accuracy of classifications of remotely sensed data","volume":"37","author":"Congalton","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1029","DOI":"10.1109\/TGRS.2006.890412","article-title":"A Multiscale Curvature Algorithm for Classifying Discrete Return LiDAR in Forested Environments","volume":"45","author":"Evans","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/8\/1443\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:30:20Z","timestamp":1760106620000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/8\/1443"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,18]]},"references-count":50,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["rs16081443"],"URL":"https:\/\/doi.org\/10.3390\/rs16081443","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,18]]}}}