{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T13:04:48Z","timestamp":1765976688813,"version":"build-2065373602"},"reference-count":29,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,6,4]],"date-time":"2023-06-04T00:00:00Z","timestamp":1685836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41961063","42064002"],"award-info":[{"award-number":["41961063","42064002"]}],"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>High-precision ground point cloud data has a wide range of applications in various fields, and the separation of ground points from non-ground points is a crucial preprocessing step. Therefore, designing an efficient, accurate, and stable ground extraction algorithm is highly significant for improving the processing efficiency and analysis accuracy of point cloud data. The study area in this article was a park in Guilin, Guangxi, China. The point cloud was obtained by utilizing the UAV platform. In order to improve the stability and accuracy of the filter algorithm, this article proposed a triangular grid filter based on the Slope Filter, found violation points by the spatial position relationship within each point in the triangulation network, improved KD-Tree-Based Euclidean Clustering, and applied it to the non-ground point extraction. This method is accurate, stable, and achieves the separation of ground points from non-ground points. Firstly, the Slope Filter is used to remove some non-ground points and reduce the error of taking ground points as non-ground points. Secondly, a triangular grid based on the triangular relationship between each point is established, and the violation triangle is determined through the grid; thus, the corresponding violation points are found in the violation triangle. Thirdly, according to the three-point collinear method to extract the regular points, these points are used to extract the regular landmarks by the KD-Tree-Based Euclidean Clustering and Convex Hull Algorithm. Finally, the dispersed points and irregular landmarks are removed by the Clustering Algorithm. In order to confirm the superiority of this algorithm, this article compared the filter effects of various algorithms on the study area and filtered the 15 data samples provided by ISPRS, obtaining an average error of 3.46%. The results show that the algorithm presented in this article has high processing efficiency and accuracy, which can significantly improve the processing efficiency of point cloud data in practical applications.<\/jats:p>","DOI":"10.3390\/rs15112930","type":"journal-article","created":{"date-parts":[[2023,6,5]],"date-time":"2023-06-05T02:18:29Z","timestamp":1685931509000},"page":"2930","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Triangular Grid Filter Method Based on the Slope Filter"],"prefix":"10.3390","volume":"15","author":[{"given":"Chuanli","family":"Kang","sequence":"first","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China"},{"name":"Key Laboratory of Spatial Information and Geomatics, Guilin University of Technology, Guilin 541004, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1938-385X","authenticated-orcid":false,"given":"Zitao","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China"}]},{"given":"Siyi","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China"}]},{"given":"Yiling","family":"Lan","sequence":"additional","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China"}]},{"given":"Chongming","family":"Geng","sequence":"additional","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China"}]},{"given":"Sai","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Lv, D., Ying, X., Cui, Y., Song, J., Qian, K., and Li, M. (2017, January 3\u20135). Research on the technology of LIDAR data processing. Proceedings of the 2017 First International Conference on Electronics Instrumentation & Information Systems (EIIS), Harbin, China.","DOI":"10.1109\/EIIS.2017.8298694"},{"key":"ref_2","unstructured":"Sithole, G., and Vosselman, G. (2003, January 8\u201310). Comparison of filtering algorithms. Proceedings of the ISPRS working group III\/3 workshop, Dresden, Germany."},{"key":"ref_3","first-page":"52","article-title":"Comparative analysis of different airborne LiDAR point cloud filtering algorithms","volume":"46","author":"Zheng","year":"2021","journal-title":"Surv. Geogr. Inf."},{"key":"ref_4","first-page":"170","article-title":"The feasibility study of DEM production based on dense matching point cloud","volume":"170","author":"Huang","year":"2022","journal-title":"Bull. Surv. Mapp."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1294","DOI":"10.3390\/rs6021294","article-title":"Segmentation-based filtering of airborne LiDAR point clouds by progressive densification of terrain segments","volume":"6","author":"Lin","year":"2014","journal-title":"Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhang, W., Qi, J., Wan, P., Wang, H., Xie, D., and Wang, X. (2016). An easy-to-use airborne LiDAR data Filter method based on cloth simulation. Remote Sens., 8.","DOI":"10.3390\/rs8060501"},{"key":"ref_7","first-page":"935","article-title":"Slope based Filter of laser altimetry data","volume":"33","author":"Vosselman","year":"2000","journal-title":"Int. Arch. Photogramm. Remote Sens."},{"key":"ref_8","first-page":"153","article-title":"Adaptive threshold point cloud Filter method for multi-level moving surface fitting","volume":"47","author":"Zhu","year":"2018","journal-title":"J. Surv. Mapp."},{"key":"ref_9","first-page":"438","article-title":"A Multi-scale Adaptive Slope Filter Algorithm for Point Cloud","volume":"47","author":"Wang","year":"2022","journal-title":"J. Wuhan Univ."},{"key":"ref_10","first-page":"313","article-title":"Reconstructing 2-D\/3-D building shapes from spaceborne tomographic SAR point clouds","volume":"40","author":"Shahzad","year":"2014","journal-title":"ISPRS"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3541","DOI":"10.1109\/TGRS.2013.2273619","article-title":"Facade reconstruction using multiview spaceborne TomoSAR point clouds","volume":"52","author":"Zhu","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4000205","DOI":"10.1109\/LGRS.2023.3234406","article-title":"KD-Tree-Based Euclidean Clustering for Tomographic SAR Point Cloud Extraction and Segmentation","volume":"20","author":"Guo","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1492","DOI":"10.1126\/science.1242072","article-title":"Clustering by fast search and find of density peaks","volume":"344","author":"Rodriguez","year":"2014","journal-title":"Science"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1111","DOI":"10.1016\/j.ins.2022.06.032","article-title":"Extraction of indoor objects based on the exponential function density clustering model","volume":"607","author":"Chen","year":"2022","journal-title":"Inf. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1440","DOI":"10.1109\/JSTARS.2013.2251457","article-title":"Aerial 3D building detection and modeling from airborne LiDAR point clouds","volume":"6","author":"Sun","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1186\/s40537-020-00374-x","article-title":"Automatic LIDAR building segmentation based on DGCNN and euclidean clustering","volume":"7","author":"Gamal","year":"2020","journal-title":"J. Big Data"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"04020033","DOI":"10.1061\/(ASCE)CP.1943-5487.0000920","article-title":"3D reconstruction and measurement of surface defects in prefabricated elements using point clouds","volume":"34","author":"Xu","year":"2020","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"927","DOI":"10.1007\/s11004-009-9244-2","article-title":"Surface-based 3D modeling of geological structures","volume":"41","author":"Caumon","year":"2009","journal-title":"Math. Geosci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1440","DOI":"10.3758\/BF03212145","article-title":"Individual differences in collinearity judgment as a function of angular position","volume":"62","author":"Greene","year":"2000","journal-title":"Percept. Psychophys."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Huang, J., Stoter, J., Peters, R., and Nan, L. (2022). City3D: Large-scale building reconstruction from airborne LiDAR point clouds. Remote Sens., 14.","DOI":"10.3390\/rs14092254"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4674","DOI":"10.3390\/app9214674","article-title":"Investigation on roof segmentation for 3D building reconstruction from aerial LIDAR point clouds","volume":"9","author":"Albano","year":"2019","journal-title":"Appl. Sci."},{"key":"ref_22","unstructured":"Huang, N.E. (2001, January 26). Review of empirical mode decomposition. Proceedings of the Aerospace\/Defense Sensing, Simulation, and Controls, Orlando, FL, USA. SPIE 4391."},{"key":"ref_23","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_24","first-page":"31","article-title":"Interpolation of high quality ground models from laser scanner data in forested areas","volume":"32","author":"Pfeifer","year":"1999","journal-title":"Int. Arch. Photogramm. Remote Sens."},{"key":"ref_25","first-page":"336","article-title":"Terrain surface reconstruction by the use of tetrahedron model with the MDL criterion","volume":"Volume 34","author":"Sohn","year":"2002","journal-title":"International Archives of Photogrammetry Remote Sensing and Spatial Information Sciences"},{"key":"ref_26","unstructured":"Elmqvist, M. (2000). Automatic Ground Modeling Using Laser Radar Data. [Master\u2019s Thesis, Linkoping University]."},{"key":"ref_27","first-page":"227","article-title":"Airborne laser scanning-clustering in raw data","volume":"34","author":"Roggero","year":"2001","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_28","unstructured":"Brovelli, M.A., Cannata, M., and Longoni, U.M. (2002, January 11\u201313). Managing and processing LIDAR data within GRASS. Proceedings of the Open source GIS\u2014GRASS Users Conference 2002, Trento, Italy."},{"key":"ref_29","first-page":"293","article-title":"Digital terrain models from airborne laserscanner data-a grid based approach","volume":"34","author":"Wack","year":"2002","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/11\/2930\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:48:01Z","timestamp":1760125681000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/11\/2930"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,4]]},"references-count":29,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["rs15112930"],"URL":"https:\/\/doi.org\/10.3390\/rs15112930","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,6,4]]}}}