{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T21:45:50Z","timestamp":1769636750140,"version":"3.49.0"},"reference-count":72,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T00:00:00Z","timestamp":1732752000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000125","name":"National Institute for Occupational Safety and Health (NIOSH)","doi-asserted-by":"publisher","award":["75D30119C05412"],"award-info":[{"award-number":["75D30119C05412"]}],"id":[{"id":"10.13039\/100000125","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Automatically identifying mine and tunnel infrastructure elements, such as rock bolts, from point cloud data improves deformation and quality control analyses and could ultimately contribute to improved safety on engineering projects. However, we hypothesize that existing methods are sensitive to small changes in object characteristics across datasets if trained insufficiently, and previous studies have only investigated single datasets. In this study, we present a cross-site training (generalization) investigation for a multi-class tunnel infrastructure classification task on terrestrial laser scanning data. In contrast to previous work, the novelty of this work is that the models are trained and tested across multiple datasets collected in different tunnels. We used two random forest (RF) implementations and one neural network (NN), as proposed in recent studies, on four datasets collected in different mines and tunnels in the US and Canada. We labeled points as belonging to one of four classes\u2014rock, bolt, mesh, and other\u2014and performed cross-site training experiments to evaluate accuracy differences between sites. In general, we found that the NN and RF models had similar performance to each other, and that same-site classification was generally successful, but cross-site performance was much lower and judged as not practically useful. Thus, our results indicate that standard geometric features are often insufficient for generalized classification of tunnel infrastructure, and these types of methods are most successful when applied to specific individual sites using interactive software for classification. Possible future research directions to improve generalized performance are discussed, including domain adaptation and deep learning methods.<\/jats:p>","DOI":"10.3390\/rs16234466","type":"journal-article","created":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T07:28:01Z","timestamp":1732778881000},"page":"4466","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Generalized Extraction of Bolts, Mesh, and Rock in Tunnel Point Clouds: A Critical Comparison of Geometric Feature-Based Methods Using Random Forest and Neural Networks"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6874-3308","authenticated-orcid":false,"given":"Luke","family":"Weidner","sequence":"first","affiliation":[{"name":"Department of Geology and Geological Engineering, Colorado School of Mines, 1400 Illinois St., Golden, CO 80401, USA"},{"name":"BGC Engineering Inc., 600 12th St., Golden, CO 80401, USA"}]},{"given":"Gabriel","family":"Walton","sequence":"additional","affiliation":[{"name":"Department of Geology and Geological Engineering, Colorado School of Mines, 1400 Illinois St., Golden, CO 80401, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.ijmst.2022.09.022","article-title":"A review of laser scanning for geological and geotechnical applications in underground mining","volume":"33","author":"Raval","year":"2023","journal-title":"Int. J. Min. Sci. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"521","DOI":"10.5194\/isprs-archives-XLI-B5-521-2016","article-title":"Automatic Thickness and Volume Estimation of Sprayed Concrete on Anchored Retaining Walls from Terrestrial Lidar Data","volume":"XLI-B5","author":"Puente","year":"2016","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_3","first-page":"239","article-title":"Automatic Crack Detection and Quantification for Tunnel Lining Surface from 3D Terrestrial LiDAR Data","volume":"11","author":"Zhou","year":"2023","journal-title":"J. Eng. Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"281","DOI":"10.5194\/isprs-annals-V-2-2020-281-2020","article-title":"Semantic Segmentation of Point Clouds with Pointnet and Kpconv Architectures Applied to Railway Tunnels","volume":"V-2-2020","author":"Riveiro","year":"2020","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.autcon.2018.09.014","article-title":"Automated detection and decomposition of railway tunnels from Mobile Laser Scanning Datasets","volume":"96","author":"Riveiro","year":"2018","journal-title":"Autom. Constr."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Fahle, L., Petruska, A.J., Walton, G., Brune, J.F., and Holley, E.A. (2023). Development and Testing of Octree-Based Intra-Voxel Statistical Inference to Enable Real-Time Geotechnical Monitoring of Large-Scale Underground Spaces with Mobile Laser Scanning Data. Remote Sens., 15.","DOI":"10.3390\/rs15071764"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.ijrmms.2018.03.004","article-title":"Change detection in drill and blast tunnels from point cloud data","volume":"105","author":"Walton","year":"2018","journal-title":"Int. J. Rock Mech. Min. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"105072","DOI":"10.1016\/j.ijrmms.2022.105072","article-title":"Automated rock mass discontinuity set characterisation using amplitude and phase decomposition of point cloud data","volume":"152","author":"Singh","year":"2022","journal-title":"Int. J. Rock Mech. Min. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"103656","DOI":"10.1016\/j.tust.2020.103656","article-title":"A machine learning approach for the detection of supporting rock bolts from laser scan data in an underground mine","volume":"107","author":"Gallwey","year":"2021","journal-title":"Tunn. Undergr. Space Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1322","DOI":"10.1109\/LGRS.2015.2398814","article-title":"Rock Surface Classification in a Mine Drift Using Multiscale Geometric Features","volume":"12","author":"Mills","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Tatsch, C., Bredu, J.A., Covell, D., Tulu, I.B., and Gu, Y. (2023, January 28\u201330). Rhino: An Autonomous Robot for Mapping Underground Mine Environments. Proceedings of the 2023 IEEE\/ASME International Conference on Advanced Intelligent Mechatronics (AIM), Seattle, WA, USA.","DOI":"10.1109\/AIM46323.2023.10196202"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Papachristos, C., Khattak, S., Mascarich, F., and Alexis, K. (2019, January 2\u20139). Autonomous Navigation and Mapping in Underground Mines Using Aerial Robots. Proceedings of the 2019 IEEE Aerospace Conference, Big Sky, MT, USA.","DOI":"10.1109\/AERO.2019.8741532"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"105326","DOI":"10.1016\/j.enggeo.2019.105326","article-title":"Classification methods for point clouds in rock slope monitoring: A novel machine learning approach and comparative analysis","volume":"263","author":"Weidner","year":"2019","journal-title":"Eng. Geol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1833","DOI":"10.1130\/GES01326.1","article-title":"An approach for automated lithological classification of point clouds","volume":"12","author":"Walton","year":"2016","journal-title":"Geosphere"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/S0924-2716(99)00008-8","article-title":"Processing of laser scanner data\u2014Algorithms and applications","volume":"54","author":"Axelsson","year":"1999","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"257","DOI":"10.5194\/isprsarchives-XL-7-W2-257-2013","article-title":"Point cloud segmentation for urban scene classification","volume":"XL-7\/W2","author":"Vosselman","year":"2013","journal-title":"ISPRS-Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.rse.2014.11.001","article-title":"Urban land cover classification using airborne LiDAR data: A review","volume":"158","author":"Yan","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Becker, C., H\u00e4ni, N., Rosinskaya, E., d\u2019Angelo, E., and Strecha, C. (2017). Classification of Aerial Photogrammetric 3D Point Clouds. arXiv.","DOI":"10.5194\/isprs-annals-IV-1-W1-3-2017"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/j.isprsjprs.2015.01.016","article-title":"Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers","volume":"105","author":"Weinmann","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"107286","DOI":"10.1016\/j.enggeo.2023.107286","article-title":"Classification of rock slope cavernous weathering on UAV photogrammetric point clouds: The example of Hegra (UNESCO World Heritage Site, Kingdom of Saudi Arabia)","volume":"325","author":"Beni","year":"2023","journal-title":"Eng. Geol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1109\/MGRS.2019.2937630","article-title":"A Review of Point Cloud Semantic Segmentation","volume":"8","author":"Xie","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Thomas, H., Deschaud, J.-E., Marcotegui, B., Goulette, F., and Gall, Y.L. (2018, January 5\u20138). Semantic Classification of 3D Point Clouds with Multiscale Spherical Neighborhoods. Proceedings of the 2018 International Conference on 3D Vision (3DV), Verona, Italy.","DOI":"10.1109\/3DV.2018.00052"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Farmakis, I., Bonneau, D., Hutchinson, D.J., and Vlachopoulos, N. (2021). Targeted Rock Slope Assessment Using Voxels and Object-Oriented Classification. Remote Sens., 13.","DOI":"10.3390\/rs13071354"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"112098","DOI":"10.1016\/j.rse.2020.112098","article-title":"A method for vegetation extraction in mountainous terrain for rockfall simulation","volume":"251","author":"Bonneau","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"105859","DOI":"10.1016\/j.envsoft.2023.105859","article-title":"Class3Dp: A supervised classifier of vegetation species from point clouds","volume":"171","author":"Estornell","year":"2024","journal-title":"Environ. Model. Softw."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"5834","DOI":"10.1109\/JSTARS.2024.3370159","article-title":"Explainable Artificial Intelligence for Machine Learning-Based Photogrammetric Point Cloud Classification","volume":"17","author":"Atik","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_27","unstructured":"Qi, C.R., Su, H., Mo, K., and Guibas, L.J. (2017, January 21\u201326). PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA."},{"key":"ref_28","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_29","doi-asserted-by":"crossref","first-page":"5663","DOI":"10.21105\/joss.05663","article-title":"samgeo: A Python package for segmenting geospatial data with the Segment Anything Model (SAM)","volume":"8","author":"Wu","year":"2023","journal-title":"J. Open Source Softw."},{"key":"ref_30","unstructured":"Yarroudh, A. (2023, December 23). LiDAR Automatic Unsupervised Segmentation using Segment-Anything Model (SAM) from Meta AI. Available online: https:\/\/github.com\/Yarroudh\/segment-lidar."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"106836","DOI":"10.1016\/j.enggeo.2022.106836","article-title":"Rockfall detection using LiDAR and deep learning","volume":"309","author":"Farmakis","year":"2022","journal-title":"Eng. Geol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"6467","DOI":"10.1109\/JSTARS.2021.3091389","article-title":"A Comparison of Deep Learning Methods for Airborne Lidar Point Clouds Classification","volume":"14","author":"Li","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.isprsjprs.2023.11.022","article-title":"3DMASC: Accessible, explainable 3D point clouds classification. Application to BI-spectral TOPO-bathymetric lidar data","volume":"207","author":"Letard","year":"2024","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"177","DOI":"10.5194\/isprs-annals-III-3-177-2016","article-title":"Fast Semantic Segmentation of 3d Point Clouds with Strongly Varying Density","volume":"III-3","author":"Hackel","year":"2016","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Hu, Q., Yang, B., Xie, L., Rosa, S., Guo, Y., Wang, Z., Trigoni, N., and Markham, A. (2020). RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds. arXiv.","DOI":"10.1109\/CVPR42600.2020.01112"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","article-title":"Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead","volume":"1","author":"Rudin","year":"2019","journal-title":"Nat. Mach. Intell."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1080\/2150704X.2020.1809734","article-title":"Roof bolt identification in underground coal mines from 3D point cloud data using local point descriptors and artificial neural network","volume":"42","author":"Singh","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.ijmst.2021.01.001","article-title":"A robust approach to identify roof bolts in 3D point cloud data captured from a mobile laser scanner","volume":"31","author":"Singh","year":"2021","journal-title":"Int. J. Min. Sci. Technol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.isprsjprs.2012.01.006","article-title":"3D terrestrial lidar data classification of complex natural scenes using a multi-scale dimensionality criterion: Applications in geomorphology","volume":"68","author":"Brodu","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"148873","DOI":"10.1109\/ACCESS.2021.3120207","article-title":"A Coarse-to-Fine Approach for Rock Bolt Detection From 3D Point Clouds","volume":"9","author":"Saydam","year":"2021","journal-title":"IEEE Access"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Li, S., Yue, D., Zheng, D., Cai, D., and Hu, C. (2022). A Geometric-Feature-Based Method for Automatic Extraction of Anchor Rod Points from Dense Point Cloud. Sensors, 22.","DOI":"10.3390\/s22239289"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"247","DOI":"10.5194\/isprs-archives-XLIII-B2-2021-247-2021","article-title":"Exploring cross-city semantic segmentation of ALS point clouds","volume":"XLIII-B2-2021","author":"Xie","year":"2021","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"5702017","DOI":"10.1109\/TGRS.2024.3364181","article-title":"Multiprototype Relational Network for Few-Shot ALS Point Cloud Semantic Segmentation by Transferring Knowledge From Photogrammetric Point Clouds","volume":"62","author":"Dai","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"106344","DOI":"10.1016\/j.enggeo.2021.106344","article-title":"The influence of training data variability on a supervised machine learning classifier for Structure from Motion (SfM) point clouds of rock slopes","volume":"294","author":"Weidner","year":"2021","journal-title":"Eng. Geol."},{"key":"ref_45","unstructured":"(2024, October 03). Leica Geosystems Leica ScanStation C10 Product Specifications. Available online: https:\/\/www.universityofgalway.ie\/media\/publicsub-sites\/engineering\/files\/Leica_ScanStation_C10_DS.pdf."},{"key":"ref_46","unstructured":"(2024, October 03). Leica Geosystems Leica HDS6000 Product Specification. Available online: https:\/\/www.geotech.sk\/OLD\/teo4_Leica%20HDS6000_EN.pdf."},{"key":"ref_47","unstructured":"FARO Technical Specification Sheet for the Focus Laser Scanner (2024, October 03). FARO\u00ae Knowledge Base. Available online: https:\/\/knowledge.faro.com\/Hardware\/Focus\/Focus\/Technical_Specification_Sheet_for_the_Focus_Laser_Scanner."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"107039","DOI":"10.1016\/j.geomorph.2020.107039","article-title":"Generalization considerations and solutions for point cloud hillslope classifiers","volume":"354","author":"Weidner","year":"2020","journal-title":"Geomorphology"},{"key":"ref_49","unstructured":"Girardeau-Montaut, D. (2021, January 30). CloudCompare, Version 2.12. Available online: https:\/\/cloudcompare.org."},{"key":"ref_50","unstructured":"Weidner, L. (2023, December 23). Terpunkto. MATLAB. Available online: https:\/\/github.com\/lmweidner\/terpunkto."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.isprsjprs.2021.04.001","article-title":"Classifying rock slope materials in photogrammetric point clouds using robust color and geometric features","volume":"176","author":"Weidner","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"157","DOI":"10.5194\/isprs-annals-IV-1-W1-157-2017","article-title":"Geometric Features and Their Relevance for 3d Point Cloud Classification","volume":"IV-1\/W1","author":"Weinmann","year":"2017","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1007\/s11222-016-9646-1","article-title":"Correlation and variable importance in random forests","volume":"27","author":"Gregorutti","year":"2017","journal-title":"Stat. Comput."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Darst, B.F., Malecki, K.C., and Engelman, C.D. (2018). Using recursive feature elimination in random forest to account for correlated variables in high dimensional data. BMC Genet., 19.","DOI":"10.1186\/s12863-018-0633-8"},{"key":"ref_55","unstructured":"Ester, M., Kriegel, H.-P., Sander, J., and Xu, X. (1996, January 2\u20134). A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96), Portland, OR, USA."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"91","DOI":"10.5194\/isprs-annals-IV-1-W1-91-2017","article-title":"Semantic3d.net: A New Large-Scale Point Cloud Classification Benchmark","volume":"IV-1\/W1","author":"Hackel","year":"2017","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"105735","DOI":"10.1016\/j.tust.2024.105735","article-title":"Seg2Tunnel: A hierarchical point cloud dataset and benchmarks for segmentation of segmental tunnel linings","volume":"147","author":"Lin","year":"2024","journal-title":"Tunn. Undergr. Space Technol."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"105829","DOI":"10.1016\/j.tust.2024.105829","article-title":"STSD:A large-scale benchmark for semantic segmentation of subway tunnel point cloud","volume":"150","author":"Cui","year":"2024","journal-title":"Tunn. Undergr. Space Technol."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1109\/JPROC.2012.2231951","article-title":"Active Learning: Any Value for Classification of Remotely Sensed Data?","volume":"101","author":"Crawford","year":"2013","journal-title":"Proc. IEEE"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1007\/s10994-021-06003-9","article-title":"How to measure uncertainty in uncertainty sampling for active learning","volume":"111","author":"Nguyen","year":"2022","journal-title":"Mach. Learn."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Matskevych, A., Wolny, A., Pape, C., and Kreshuk, A. (2022). From Shallow to Deep: Exploiting Feature-Based Classifiers for Domain Adaptation in Semantic Segmentation. Front. Comput. Sci., 4.","DOI":"10.3389\/fcomp.2022.805166"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1811","DOI":"10.1109\/TPAMI.2016.2618118","article-title":"Learn on Source, Refine on Target:A Model Transfer Learning Framework with Random Forests","volume":"39","author":"Segev","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"2429","DOI":"10.1007\/s11063-022-10977-5","article-title":"A Survey on Adversarial Domain Adaptation","volume":"55","author":"Seydi","year":"2023","journal-title":"Neural Process. Lett."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Anthony, P., Ishizuka, M., and Lukose, D. (2012). A Feature Space Alignment Learning Algorithm. PRICAI 2012: Trends in Artificial Intelligence, Springer.","DOI":"10.1007\/978-3-642-32695-0"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"15629","DOI":"10.1007\/s00521-023-08455-7","article-title":"A multi-granularity semisupervised active learning for point cloud semantic segmentation","volume":"35","author":"Ye","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Karantanellis, E., Marinos, V., Vassilakis, E., and Christaras, B. (2020). Object-Based Analysis Using Unmanned Aerial Vehicles (UAVs) for Site-Specific Landslide Assessment. Remote Sens., 12.","DOI":"10.3390\/rs12111711"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1049","DOI":"10.5194\/isprs-archives-XLIII-B2-2020-1049-2020","article-title":"Supervoxel-Based Multi-Scale Point Cloud Segmentation Using Fnea for Object-Oriented Rock Slope Classification Using Tls","volume":"XLIII-B2-2020","author":"Farmakis","year":"2020","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Liao, L., Tang, S., Liao, J., Li, X., Wang, W., Li, Y., and Guo, R. (2022). A Supervoxel-Based Random Forest Method for Robust and Effective Airborne LiDAR Point Cloud Classification. Remote Sens., 14.","DOI":"10.3390\/rs14061516"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"843","DOI":"10.5194\/isprs-archives-XLII-4-W18-843-2019","article-title":"Aerial Point Cloud Classification with Deep Learning and Machine Learning Algorithms","volume":"XLII-4-W18","author":"Remondino","year":"2019","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Zhang, Y.-M., Cheng, C.-Y., Lin, C.-L., Lee, C.-C., and Fan, K.-C. (2023). Develop a Lightweight Convolutional Neural Network to Recognize Palms Using 3D Point Clouds. Information, 14.","DOI":"10.3390\/info14070381"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Imad, M., Doukhi, O., and Lee, D.-J. (2021). Transfer Learning Based Semantic Segmentation for 3D Object Detection from Point Cloud. Sensors, 21.","DOI":"10.3390\/s21123964"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/bs.adcom.2020.11.003","article-title":"Chapter Six\u2014Deep learning with GPUs","volume":"Volume 122","author":"Kim","year":"2021","journal-title":"Advances in Computers"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/23\/4466\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:41:36Z","timestamp":1760114496000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/23\/4466"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,28]]},"references-count":72,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["rs16234466"],"URL":"https:\/\/doi.org\/10.3390\/rs16234466","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,28]]}}}