{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:12:27Z","timestamp":1760112747610,"version":"build-2065373602"},"reference-count":74,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,8]],"date-time":"2024-08-08T00:00:00Z","timestamp":1723075200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"OFB through the AUPASED project"},{"name":"European Regional Development Fund and the Normandie Region for the CHERLOC project"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This study introduces a new software, cLASpy_T, that helps design models for the automatic 3D point cloud classification of coastal environments. This software is based on machine learning algorithms from the scikit-learn library and can classify point clouds derived from LiDAR or photogrammetry. Input data can be imported via CSV or LAS files, providing a 3D point cloud, enhanced with geometric features or spectral information, such as colors from orthophotos or hyperspectral data. cLASpy_T lets the user run three supervised machine learning algorithms from the scikit-learn API to build automatic classification models: RandomForestClassifier, GradientBoostingClassifier and MLPClassifier. This work presents the general method for classification model design using cLASpy_T and the software\u2019s complete workflow with an example of photogrammetry point cloud classification. Four photogrammetric models of a coastal dike were acquired on four different dates, in 2021. The aim is to classify each point according to whether it belongs to the \u2018sand\u2019 class of the beach, the \u2018rock\u2019 class of the riprap, or the \u2018block\u2019 class of the concrete blocks. This case study highlights the importance of adjusting algorithm parameters, selecting features, and the large number of tests necessary to design a classification model that can be generalized and used in production.<\/jats:p>","DOI":"10.3390\/rs16162891","type":"journal-article","created":{"date-parts":[[2024,8,8]],"date-time":"2024-08-08T07:01:25Z","timestamp":1723100485000},"page":"2891","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A New Open-Source Software to Help Design Models for Automatic 3D Point Cloud Classification in Coastal Studies"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8029-574X","authenticated-orcid":false,"given":"Xavier","family":"Pellerin Le Bas","sequence":"first","affiliation":[{"name":"Scienteama, 4 Avenue de Cambridge, 14200 H\u00e9rouville-Saint-Clair, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3192-7389","authenticated-orcid":false,"given":"Laurent","family":"Froideval","sequence":"additional","affiliation":[{"name":"Normandie Univ, UNICAEN, UNIROUEN, CNRS, M2C, 14000 Caen, France"}]},{"given":"Adan","family":"Mouko","sequence":"additional","affiliation":[{"name":"Normandie Univ, UNICAEN, UNIROUEN, CNRS, M2C, 14000 Caen, France"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-2635-8919","authenticated-orcid":false,"given":"Christophe","family":"Conessa","sequence":"additional","affiliation":[{"name":"Normandie Univ, UNICAEN, UNIROUEN, CNRS, M2C, 14000 Caen, France"}]},{"given":"Laurent","family":"Benoit","sequence":"additional","affiliation":[{"name":"Normandie Univ, UNICAEN, UNIROUEN, CNRS, M2C, 14000 Caen, France"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-7180-5277","authenticated-orcid":false,"given":"Laurent","family":"Perez","sequence":"additional","affiliation":[{"name":"Normandie Univ, UNICAEN, UNIROUEN, CNRS, M2C, 14000 Caen, France"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1109\/MGRS.2019.2937630","article-title":"Linking Points With Labels in 3D: A Review of Point Cloud Semantic Segmentation","volume":"8","author":"Xie","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Sturdivant, E., Lentz, E., Thieler, E.R., Farris, A., Weber, K., Remsen, D., Miner, S., and Henderson, R. (2017). UAS-SfM for Coastal Research: Geomorphic Feature Extraction and Land Cover Classification from High-Resolution Elevation and Optical Imagery. Remote Sens., 9.","DOI":"10.3390\/rs9101020"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.geomorph.2017.12.037","article-title":"Coastal Monitoring Solutions of the Geomorphological Response of Beach-Dune Systems Using Multi-Temporal LiDAR Datasets (Vend\u00c3\u00a9 e Coast, France)","volume":"304","author":"Juigner","year":"2018","journal-title":"Geomorphology"},{"key":"ref_4","first-page":"esp.5236","article-title":"The Formation and Morphodynamics of Complex Multi-hooked Spits and the Contribution of Swash Bars","volume":"47","author":"Levoy","year":"2021","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2921","DOI":"10.1080\/01431161.2016.1277044","article-title":"Evaluation of the Accuracy of Lidar Data Acquired Using a UAS for Levee Monitoring: Preliminary Results","volume":"38","author":"Bakula","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"359","DOI":"10.5194\/isprs-annals-V-2-2022-359-2022","article-title":"Efficient Dike Monitoring Using Terrestrial SfM Photogrammetry","volume":"2","author":"Froideval","year":"2022","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Gon\u00e7alves, D., Gon\u00e7alves, G., P\u00e9rez-Alvavez, J.A., and Andriolo, U. (2022). On the 3D Reconstruction of Coastal Structures by Unmanned Aerial Systems with Onboard Global Navigation Satellite System and Real-Time Kinematics and Terrestrial Laser Scanning. Remote Sens., 14.","DOI":"10.3390\/rs14061485"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.geomorph.2012.08.021","article-title":"\u2018Structure-from-Motion\u2019 Photogrammetry: A Low-Cost, Effective Tool for Geoscience Applications","volume":"179","author":"Westoby","year":"2012","journal-title":"Geomorphology"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1097","DOI":"10.1007\/s10346-017-0942-4","article-title":"High-Resolution Monitoring of Complex Coastal Morphology Changes: Cross-Efficiency of SfM and TLS-Based Survey (Vaches-Noires Cliffs, Normandy, France)","volume":"15","author":"Medjkane","year":"2018","journal-title":"Landslides"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"phor.12297","DOI":"10.1111\/phor.12297","article-title":"A Low-cost Open-source Workflow to Generate Georeferenced 3D SfM Photogrammetric Models of Rocky Outcrops","volume":"34","author":"Froideval","year":"2019","journal-title":"Photogram Rec."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/j.ifacol.2017.08.046","article-title":"Map-Based Localization Method for Autonomous Vehicles Using 3D-LIDAR","volume":"50","author":"Wang","year":"2017","journal-title":"IFAC-Pap. OnLine"},{"key":"ref_12","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 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01112"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.landusepol.2013.08.004","article-title":"A Geometric and Semantic Evaluation of 3D Data Sourcing Methods for Land and Property Information","volume":"36","author":"Jazayeri","year":"2014","journal-title":"Land Use Policy"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Li, J., Liu, J., and Huang, Q. (2023). PointDMM: A Deep-Learning-Based Semantic Segmentation Method for Point Clouds in Complex Forest Environments. Forests, 14.","DOI":"10.3390\/f14122276"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Mukhandi, H., Ferreira, J.F., and Peixoto, P. (2024). SyS3DS: Systematic Sampling of Large-Scale LiDAR Point Clouds for Semantic Segmentation in Forestry Robotics. Sensors, 24.","DOI":"10.3390\/s24030823"},{"key":"ref_16","unstructured":"Pellerin Le Bas, X. (2024, August 04). cLASpy_T v0.3. Available online: https:\/\/github.com\/TrickyPells\/cLASpy_T."},{"key":"ref_17","first-page":"248","article-title":"Segmentation of Point Clouds Using Smoothness Constraint","volume":"36","author":"Rabbani","year":"2006","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_18","unstructured":"Bhanu, B., Lee, S., Ho, C.-C., and Henderson, T.C. (1986, January 27\u201331). Range Data Processing: Representation of Surfaces by Edges. Proceedings of the Eighth International Conference on Pattern Recognition, Paris, France."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.isprsjprs.2015.01.011","article-title":"Octree-Based Region Growing for Point Cloud Segmentation","volume":"104","author":"Vo","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/0167-8655(90)90042-Z","article-title":"A New Curve Detection Method: Randomized Hough Transform (RHT)","volume":"11","author":"Xu","year":"1990","journal-title":"Pattern Recognit. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1145\/358669.358692","article-title":"Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography","volume":"24","author":"Fischler","year":"1981","journal-title":"Commun. ACM"},{"key":"ref_22","first-page":"119","article-title":"Surface Clustering from Airborne Laser Scanning Data","volume":"34","author":"Filin","year":"2002","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4270","DOI":"10.1109\/JSTARS.2018.2817227","article-title":"Unsupervised Segmentation of Point Clouds From Buildings Using Hierarchical Clustering Based on Gestalt Principles","volume":"11","author":"Xu","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"271","DOI":"10.5194\/isprsannals-II-3-W4-271-2015","article-title":"Contextual Classification of Point Cloud Data by Exploiting Individual 3d Neigbourhoods","volume":"II-3\/W4","author":"Weinmann","year":"2015","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Su, H., Maji, S., Kalogerakis, E., and Learned-Miller, E. (2015, January 7\u201313). Multi-View Convolutional Neural Networks for 3D Shape Recognition. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.114"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Maturana, D., and Scherer, S. (October, January 28). VoxNet: A 3D Convolutional Neural Network for Real-Time Object Recognition. Proceedings of the 2015 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany.","DOI":"10.1109\/IROS.2015.7353481"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3749","DOI":"10.3390\/rs5083749","article-title":"SVM-Based Classification of Segmented Airborne LiDAR Point Clouds in Urban Areas","volume":"5","author":"Zhang","year":"2013","journal-title":"Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.isprsjprs.2017.03.010","article-title":"Contextual Segment-Based Classification of Airborne Laser Scanner Data","volume":"128","author":"Vosselman","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_29","unstructured":"M\u00fcller, A.C., and Guido, S. (2016). Introduction to Machine Learning with Python: A Guide for Data Scientists, O\u2019Reilly. [1st ed.]."},{"key":"ref_30","unstructured":"Qian, G., Hamdi, A., Zhang, X., and Ghanem, B. (2022). Pix4Point: Image Pretrained Standard Transformers for 3D Point Cloud Understanding. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Teruggi, S., Grilli, E., Russo, M., Fassi, F., and Remondino, F. (2020). A Hierarchical Machine Learning Approach for Multi-Level and Multi-Resolution 3D Point Cloud Classification. Remote Sens., 12.","DOI":"10.3390\/rs12162598"},{"key":"ref_32","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_33","first-page":"85","article-title":"DEM Generation from Laser Scanner Data Using Adaptive Tin Models","volume":"33 (Part B3)","author":"Axelsson","year":"2000","journal-title":"Int. Arch. Photogramm. Remote Sens."},{"key":"ref_34","unstructured":"(2024, August 04). Arttu Soininen Terrasolid TerraScan. Available online: https:\/\/terrasolid.com."},{"key":"ref_35","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_36","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_37","unstructured":"Gaydon, C. (2022). Myria3D: Deep Learning for the Semantic Segmentation of Aerial Lidar Point Clouds, IGN (French Mapping Agency)."},{"key":"ref_38","unstructured":"Falcon, W. (2024, August 04). The PyTorch Lightning Team PyTorch Lightning 2019. Available online: https:\/\/github.com\/Lightning-AI\/pytorch-lightning."},{"key":"ref_39","first-page":"2825","article-title":"Scikit-Learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_40","unstructured":"Buitinck, L., Louppe, G., Blondel, M., Pedregosa, F., Mueller, A., Grisel, O., Niculae, V., Prettenhofer, P., Gramfort, A., and Grobler, J. (2013, January 23-27). API Design for Machine Learning Software: Experiences from the Scikit-Learn Project. Proceedings of the ECML PKDD Workshop: Languages for Data Mining and Machine Learning, Prague, The Czech Republic."},{"key":"ref_41","unstructured":"Froideval, L., Conessa, C., and Laurent, B. (2024, August 04). 3D Model Time Series of a Coastal Dike in Ouistreham, France 2024, 3258744154 Bytes. Available online: https:\/\/doi.org\/10.6084\/m9.figshare.25908823.v1."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Hackel, T., Savinov, N., Ladicky, L., Wegner, J.D., Schindler, K., and Pollefeys, M. (2017). Semantic3D.Net: A New Large-Scale Point Cloud Classification Benchmark. arXiv.","DOI":"10.5194\/isprs-annals-IV-1-W1-91-2017"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Hu, Q., Yang, B., Khalid, S., Xiao, W., Trigoni, N., and Markham, A. (2021, January 20\u201325). Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00494"},{"key":"ref_44","unstructured":"Gaydon, C., Daab, M., and Roche, F. (2024). FRACTAL: An Ultra-Large-Scale Aerial Lidar Dataset for 3D Semantic Segmentation of Diverse Landscapes. arXiv."},{"key":"ref_45","unstructured":"Gaydon, C., and Roche, F. (2024). PureForest: A Large-Scale Aerial Lidar and Aerial Imagery Dataset for Tree Species Classification in Monospecific Forests. arXiv."},{"key":"ref_46","unstructured":"(2024, August 04). Artelnics OpenNN. Available online: https:\/\/github.com\/Artelnics\/OpenNN."},{"key":"ref_47","first-page":"1799","article-title":"The Shogun Machine Learning Toolbox","volume":"11","author":"Sonnenburg","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref_48","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. (2015). TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. arXiv."},{"key":"ref_49","first-page":"281","article-title":"Random Search for Hyper-Parameter Optimization","volume":"13","author":"Bergstra","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1080\/00401706.1974.10489157","article-title":"The Relationship Between Variable Selection and Data Agumentation and a Method for Prediction","volume":"16","author":"Allen","year":"1974","journal-title":"Technometrics"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1111\/j.2517-6161.1974.tb00994.x","article-title":"Cross-Validatory Choice and Assessment of Statistical Predictions","volume":"36","author":"Stone","year":"1974","journal-title":"J. R. Stat. Soc. Ser. B Stat. Methodol."},{"key":"ref_52","first-page":"801","article-title":"Arcing Classifiers","volume":"26","author":"Breiman","year":"1998","journal-title":"Ann. Stat."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy Function Approximation: A Gradient Boosting Machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/S0167-9473(01)00065-2","article-title":"Stochastic Gradient Boosting","volume":"38","author":"Friedman","year":"2002","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J.H. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer. [2nd ed.].","DOI":"10.1007\/978-0-387-84858-7"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/0004-3702(89)90049-0","article-title":"Connectionist Learning Procedures","volume":"40","author":"Hinton","year":"1989","journal-title":"Artif. Intell."},{"key":"ref_58","unstructured":"Teh, Y.W., and Titterington, M. (2010, January 13). Understanding the Difficulty of Training Deep Feedforward Neural Networks. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, Chia Laguna Resort, Sardinia, Italy."},{"key":"ref_59","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 11\u201318). Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. Proceedings of the International Conference on Computer Vision, Las Condes, Chile.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_61","unstructured":"Jolliffe, I.T. (2002). Principal Component Analysis, Springer. [2nd ed.]."},{"key":"ref_62","unstructured":"QGIS Development Team (2024). QGIS 3.34 Geographic Information System, QGIS Development Team."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Kerautret, B., Colom, M., and Monasse, P. (2017). OpenMVG: Open Multiple View Geometry. Reproducible Research in Pattern Recognition: First International Workshop, Proceedings of the Reproducible Research in Pattern Recognition, Canc\u00fan, Mexico, 4 December 2016, Springer International Publishing.","DOI":"10.1007\/978-3-319-56414-2"},{"key":"ref_64","unstructured":"Cernea, D. (2024, August 04). OpenMVS v2.3.0: Multi-View Stereo Reconstruction Library. Available online: https:\/\/github.com\/cdcseacave\/openMVS."},{"key":"ref_65","unstructured":"Bisnath, S., Uijt de Haag, M., Diggle, D.W., Hegarty, C., Milbert, D., and Walter, T. (2017). Differential GNSS and Precise Point Positioning. Understanding GPS\/GNSS: Principles and Applications, Artech House."},{"key":"ref_66","unstructured":"Girardeau-Montaut, D. (2024, August 04). CloudCompare (Version 2.13.1) [GPL Software]; 2024. Available online: https:\/\/www.danielgm.net\/cc\/."},{"key":"ref_67","unstructured":"Cignoni, P., Callieri, M., Corsini, M., Dellepiane, M., Ganovelli, F., and Ranzuglia, G. (2008, January 25\u201328). MeshLab: An Open-Source Mesh Processing Tool. Proceedings of the European Interdisciplinary Cybersecurity Conference, Edinburgh, UK."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1109\/TIT.1982.1056489","article-title":"Least Squares Quantization in PCM","volume":"28","author":"Lloyd","year":"1982","journal-title":"IEEE Trans. Inform. Theory"},{"key":"ref_69","unstructured":"MacQueen, J. (1967, January 1). Some Methods for Classification and Analysis of Multivariate Observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Oakland, CA, USA."},{"key":"ref_70","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 KDD, Portland, OR, USA."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Hackel, T., Wegner, J.D., and Schindler, K. (2016, January 27\u201330). Contour Detection in Unstructured 3D Point Clouds. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.178"},{"key":"ref_72","unstructured":"Froideval, L., Monfort, O., Benoit, L., and Bonte, Y. (2024, August 04). Mesure Topographique par LiDAR A\u00e9roport\u00e9 et Mod\u00e8le Num\u00e9rique Terrain de l\u2019estuaire de l\u2019Orne du 03\/06\/2015. Available online: https:\/\/sextant.ifremer.fr\/record\/93c7ba76-9a65-4144-8802-acb86343cc47."},{"key":"ref_73","unstructured":"Froideval, L., Pellerin Le Bas, X., Conessa, C., and Laurent, B. (2024, August 04). ALS Orne Estuary ML Labeled Data 2024, 6597898321 Bytes. Available online: https:\/\/doi.org\/10.6084\/m9.figshare.26346709.v1."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1162\/089976699300016728","article-title":"Mixtures of Probabilistic Principal Component Analyzers","volume":"11","author":"Tipping","year":"1999","journal-title":"Neural Comput."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/16\/2891\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:31:55Z","timestamp":1760110315000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/16\/2891"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,8]]},"references-count":74,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["rs16162891"],"URL":"https:\/\/doi.org\/10.3390\/rs16162891","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2024,8,8]]}}}