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By comparing with a symmetric uniform reference model, this method can efficiently describe distribution patterns and detect non-uniform regions. Furthermore, a deep learning model trained on these skewness features achieves 85.96% accuracy in automated boundary extraction, significantly reducing omission errors compared to conventional density-based methods. The proposed framework offers an effective solution for automated point cloud segmentation and modeling.<\/jats:p>","DOI":"10.3390\/sym17101770","type":"journal-article","created":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T13:54:41Z","timestamp":1760968481000},"page":"1770","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Skewness-Based Density Metric and Deep Learning Framework for Point Cloud Analysis: Detection of Non-Uniform Regions and Boundary Extraction"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6469-8741","authenticated-orcid":false,"given":"Cheng","family":"Li","sequence":"first","affiliation":[{"name":"The School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianghong","family":"Hua","sequence":"additional","affiliation":[{"name":"The School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenbo","family":"Wang","sequence":"additional","affiliation":[{"name":"The School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6230-4494","authenticated-orcid":false,"given":"Pengju","family":"Tian","sequence":"additional","affiliation":[{"name":"The Engineering Research Center of Environmental Laser Remote Sensing Technology and Application of Henan Province, Nanyang Normal University, Nanyang 473061, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Tang, L.Y., Zhan, Y.B., Chen, Z., Yu, B., and Tao, D. (2022, January 18\u201324). Contrastive boundary learning for point cloud segmentation. Proceedings of the 2022 IEEE\/CVF Conference Computer Vision and Pattern Recognition Conference (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00830"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e4209","DOI":"10.1002\/ecs2.4209","article-title":"Evaluating the sensitivity of forest structural diversity characterization to LiDAR point density","volume":"13","author":"Larue","year":"2022","journal-title":"Ecosphere"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Petras, V., Petrasova, A., McCarter, J.B., Mitasova, H., and Meentemeyer, R.K. (2023). Point Density Variations in Airborne Lidar Point Clouds. Sensors, 23.","DOI":"10.3390\/s23031593"},{"key":"ref_4","first-page":"423","article-title":"Density-Based Method For Building Detection From Lidar Point Cloud","volume":"X-4\/W1-2022","author":"Mahphood","year":"2023","journal-title":"ISPRS Ann. Photogramm. 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