{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T13:50:15Z","timestamp":1772113815532,"version":"3.50.1"},"reference-count":82,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2024,9,25]],"date-time":"2024-09-25T00:00:00Z","timestamp":1727222400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Parco Spina Verde, Como (IT)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The interest in the enhancement of innovative solutions in the geospatial data classification domain from integrated aerial methods is rapidly growing. The transition from unstructured to structured information is essential to set up and arrange geodatabases and cognitive systems such as digital twins capable of monitoring territorial, urban, and general conditions of natural and\/or anthropized space, predicting future developments, and considering risk prevention. This research is based on the study of classification methods and the consequent segmentation of low-altitude airborne LiDAR data in highly forested areas. In particular, the proposed approaches investigate integrating unsupervised classification methods and supervised Neural Network strategies, starting from unstructured point-based data formats. Furthermore, the research adopts Machine Learning classification methods for geo-morphological analyses derived from DTM datasets. This paper also discusses the results from a comparative perspective, suggesting possible generalization capabilities concerning the case study investigated.<\/jats:p>","DOI":"10.3390\/rs16193572","type":"journal-article","created":{"date-parts":[[2024,9,25]],"date-time":"2024-09-25T17:12:04Z","timestamp":1727284324000},"page":"3572","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Semantic Mapping of Landscape Morphologies: Tuning ML\/DL Classification Approaches for Airborne LiDAR Data"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4864-6703","authenticated-orcid":false,"given":"Marco","family":"Cappellazzo","sequence":"first","affiliation":[{"name":"LabG4CH\u2014Laboratory of Geomatics for Cultural Heritage, Department of Architecture and Design (DAD), Politecnico di Torino, Viale Mattioli 39, 10125 Turin, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3061-5316","authenticated-orcid":false,"given":"Giacomo","family":"Patrucco","sequence":"additional","affiliation":[{"name":"LabG4CH\u2014Laboratory of Geomatics for Cultural Heritage, Department of Architecture and Design (DAD), Politecnico di Torino, Viale Mattioli 39, 10125 Turin, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7474-1544","authenticated-orcid":false,"given":"Giulia","family":"Sammartano","sequence":"additional","affiliation":[{"name":"LabG4CH\u2014Laboratory of Geomatics for Cultural Heritage, Department of Architecture and Design (DAD), Politecnico di Torino, Viale Mattioli 39, 10125 Turin, Italy"},{"name":"Polito FULL|The Future Urban Legacy Lab, OGR Tech., Corso Castelfidardo, 22, 10128 Turin, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9841-0531","authenticated-orcid":false,"given":"Marco","family":"Baldo","sequence":"additional","affiliation":[{"name":"Turin Support Unit, CNR IRPI\u2014Research Institute for Geo-Hydrological Protection, Italian National Research Council, Strada delle Cacce 73, 10135 Turin, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4243-7959","authenticated-orcid":false,"given":"Antonia","family":"Span\u00f2","sequence":"additional","affiliation":[{"name":"LabG4CH\u2014Laboratory of Geomatics for Cultural Heritage, Department of Architecture and Design (DAD), Politecnico di Torino, Viale Mattioli 39, 10125 Turin, Italy"},{"name":"Polito FULL|The Future Urban Legacy Lab, OGR Tech., Corso Castelfidardo, 22, 10128 Turin, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,25]]},"reference":[{"key":"ref_1","unstructured":"Robinson, H. (1977). Morphology and Landscape, University Tutorial Press. [3rd ed.]."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Shukla, T., Tang, W., Trettin, C.C., Chen, G., Chen, S., and Allan, C. (2023). Quantification of Microtopography in Natural Ecosystems Using Close-Range Remote Sensing. Remote Sens., 15.","DOI":"10.3390\/rs15092387"},{"key":"ref_3","first-page":"61","article-title":"A LiDAR Application to Assess Long-Term Bed-Level Changes in a Cobble-Bed River: The Case of the Orco River (North-Western Italy)","volume":"33","author":"Turitto","year":"2010","journal-title":"Geogr. Fis. Din. Quat."},{"key":"ref_4","first-page":"2","article-title":"Integrating Lidar Canopy Height Models with Satellite-Assisted Inventory Methods: A Comparison of Inventory Estimates","volume":"70","author":"Hemingway","year":"2024","journal-title":"For. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/S0169-5347(03)00071-5","article-title":"From Space to Species: Ecological Applications for Remote Sensing","volume":"18","author":"Kerr","year":"2003","journal-title":"Trends Ecol. Evol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"6880","DOI":"10.3390\/rs5126880","article-title":"Using Unmanned Aerial Vehicles (UAV) for High-Resolution Reconstruction of Topography: The Structure from Motion Approach on Coastal Environments","volume":"5","author":"Mancini","year":"2013","journal-title":"Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3283","DOI":"10.5194\/essd-16-3283-2024","article-title":"Multitemporal Characterisation of a Proglacial System: A Multidisciplinary Approach","volume":"16","author":"Corte","year":"2023","journal-title":"Earth Syst. Sci. Data Discuss."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"857","DOI":"10.1007\/s10346-015-0622-1","article-title":"Effectiveness of Visually Analyzing LiDAR DTM Derivatives for Earth and Debris Slide Inventory Mapping for Statistical Susceptibility Modeling","volume":"13","author":"Petschko","year":"2016","journal-title":"Landslides"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"9600","DOI":"10.3390\/rs6109600","article-title":"Remote Sensing for Landslide Investigations: An Overview of Recent Achievements and Perspectives","volume":"6","author":"Scaioni","year":"2014","journal-title":"Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Forte, M., and Campana, S. (2016). Digital Methods and Remote Sensing in Archaeology, Springer.","DOI":"10.1007\/978-3-319-40658-9"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.isprsjprs.2014.02.013","article-title":"Unmanned Aerial Systems for Photogrammetry and Remote Sensing: A Review","volume":"92","author":"Colomina","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"75","DOI":"10.5194\/isprs-archives-XLIII-B2-2020-75-2020","article-title":"Oblique Images and Direct Photogrammetry with a Fixed Wing Platform: First Test and Results in Hierapolis of Phrygia (TK)","volume":"XLIII-B2-2020","author":"Sammartano","year":"2020","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_13","first-page":"e00172","article-title":"UAV-Based Photogrammetry: Assessing the Application Potential and Effectiveness for Archaeological Monitoring and Surveying in the Research on the \u2018Valley of the Kings\u2019 (Tuva, Russia)","volume":"20","author":"Vavulin","year":"2021","journal-title":"Digit. Appl. Archaeol. Cult. Herit."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Lovitt, J., Rahman, M.M., and McDermid, G.J. (2017). Assessing the Value of UAV Photogrammetry for Characterizing Terrain in Complex Peatlands. Remote Sens., 9.","DOI":"10.3390\/rs9070715"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kalacska, M., Arroyo-Mora, J.P., and Lucanus, O. (2021). Comparing UAS LiDAR and Structure-from-Motion Photogrammetry for Peatland Mapping and Virtual Reality (VR) Visualization. Drones, 5.","DOI":"10.3390\/drones5020036"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"254","DOI":"10.3390\/geomatics2030015","article-title":"Quality Assessment of DJI Zenmuse L1 and P1 LiDAR and Photogrammetric Systems: Metric and Statistics Analysis with the Integration of Trimble SX10 Data","volume":"2","author":"Diara","year":"2022","journal-title":"Geomatics"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"365","DOI":"10.5194\/isprs-archives-XLVI-2-W1-2022-365-2022","article-title":"Seeing among Foliage with LiDaR and Machine Learning: Towards a Transferable Archaeological Pipeline","volume":"XLVI-2-W1-2022","author":"Mazzacca","year":"2022","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"882","DOI":"10.1016\/j.jas.2007.06.013","article-title":"Archaeological Prospection of Forested Areas Using Full-Waveform Airborne Laser Scanning","volume":"35","author":"Doneus","year":"2008","journal-title":"J. Archaeol. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Poux, F., and Billen, R. (2019). Voxel-Based 3D Point Cloud Semantic Segmentation: Unsupervised Geometric and Relationship Featuring vs Deep Learning Methods. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8050213"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2438","DOI":"10.1111\/tgis.12824","article-title":"Terraces Mapping by Using Deep Learning Approach from Remote Sensing Images and Digital Elevation Models","volume":"25","author":"Zhao","year":"2021","journal-title":"Trans. GIS"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"357","DOI":"10.5194\/isprs-archives-XLVIII-M-2-2023-357-2023","article-title":"Integrated Airborne LiDAR-UAV Methods for Archaeological Mapping in Vegetation-Covered Areas","volume":"XLVIII-M-2\u20132023","author":"Cappellazzo","year":"2023","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Albrecht, C.M., Fisher, C., Freitag, M., Hamann, H.F., Pankanti, S., Pezzutti, F., and Rossi, F. (2019, January 9\u201312). Learning and Recognizing Archeological Features from LiDAR Data. Proceedings of the 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA.","DOI":"10.1109\/BigData47090.2019.9005548"},{"key":"ref_23","first-page":"71","article-title":"Comparison of Filtering Algorithms","volume":"34","author":"Sithole","year":"2003","journal-title":"Proc. ISPRS Work. Group III\/3 Workshop"},{"key":"ref_24","first-page":"93","article-title":"LAS 1.4 Specification","volume":"78","author":"Graham","year":"2012","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_25","unstructured":"Maas, H.-G., and Vosselman, G. (2010). Airborne and Terrestrial Laser Scanning, Whittles Publishing."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.rse.2006.10.013","article-title":"Repetitive Interpolation: A Robust Algorithm for DTM Generation from Aerial Laser Scanner Data in Forested Terrain","volume":"108","author":"Kobler","year":"2007","journal-title":"Remote Sens. Environ."},{"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":"647","DOI":"10.1007\/s41324-018-0209-8","article-title":"A Review of Landform Classification Methods","volume":"26","author":"Mokarram","year":"2018","journal-title":"Spat. Inf. Res."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Colucci, E., Xing, X., Kokla, M., Mostafavi, M.A., Noardo, F., and Span\u00f2, A. (2021). Ontology-Based Semantic Conceptualisation of Historical Built Heritage to Generate Parametric Structured Models from Point Clouds. Appl. Sci., 11.","DOI":"10.3390\/app11062813"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"79","DOI":"10.5194\/isprsarchives-XXXVIII-4-C21-79-2011","article-title":"Lidar Mapping Technology to Populate Green Areas GIS","volume":"XXXVIII-4\/C21","author":"Cattaneo","year":"2011","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Masini, N., Coluzzi, R., Lasaponara, R., Masini, N., Coluzzi, R., and Lasaponara, R. (2011). On the Airborne Lidar Contribution in Archaeology: From Site Identification to Landscape Investigation. Laser Scanning, Theory and Applications, BoD\u2013Books on Demand.","DOI":"10.5772\/14655"},{"key":"ref_32","unstructured":"Doneus, M., and Neubauer, W. (October, January 26). 3D Laser Scanners on Archaeological Excavations. Proceedings of the CIPA 2005 XX International Symposium 2005: International Cooperation to Save the World\u2019s Cultural Heritage, Torino, Italy."},{"key":"ref_33","first-page":"92","article-title":"Archaeological Ground Point Filtering of Airborne Laser Scan Derived Point-Clouds in a Difficult Mediterranean Environment","volume":"3","author":"Doneus","year":"2020","journal-title":"J. Comput. Appl. Archaeol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"9951","DOI":"10.3390\/rs6109951","article-title":"Now You See It\u2026 Now You Don\u2019t: Understanding Airborne Mapping LiDAR Collection and Data Product Generation for Archaeological Research in Mesoamerica","volume":"6","author":"Carter","year":"2014","journal-title":"Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"012012","DOI":"10.1088\/1755-1315\/1127\/1\/012012","article-title":"Analysis of Airborne LiDAR Data for Archaeology Study Case: Sriwijaya Muaro Jambi Site","volume":"1127","author":"Haryuatmanto","year":"2023","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Golden, C., Scherer, A.K., Schroder, W., Murtha, T., Morell-Hart, S., Fernandez Diaz, J.C., Jim\u00e9nez \u00c1lvarez, S.D.P., Alcover Firpi, O., Agostini, M., and Bazarsky, A. (2021). Airborne Lidar Survey, Density-Based Clustering, and Ancient Maya Settlement in the Upper Usumacinta River Region of Mexico and Guatemala. Remote Sens., 13.","DOI":"10.3390\/rs13204109"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1080\/01431160600746456","article-title":"A Survey of Image Classification Methods and Techniques for Improving Classification Performance","volume":"28","author":"Lu","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2784","DOI":"10.1080\/01431161.2018.1433343","article-title":"Implementation of Machine-Learning Classification in Remote Sensing: An Applied Review","volume":"39","author":"Maxwell","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"107365","DOI":"10.1016\/j.geomorph.2020.107365","article-title":"Machine-Learning Classification of Debris-Covered Glaciers Using a Combination of Sentinel-1\/-2 (SAR\/Optical), Landsat 8 (Thermal) and Digital Elevation Data","volume":"369","author":"Alifu","year":"2020","journal-title":"Geomorphology"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"4384","DOI":"10.1080\/01431161.2015.1083632","article-title":"Differentiating Mine-Reclaimed Grasslands from Spectrally Similar Land Cover Using Terrain Variables and Object-Based Machine Learning Classification","volume":"36","author":"Maxwell","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_41","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_42","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1037\/h0042519","article-title":"The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain","volume":"65","author":"Rosenblatt","year":"1958","journal-title":"Psychol. Rev."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning Representations by Back-Propagating Errors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Vargas, R., Mosavi, A., and Ruiz, R. (2018). Deep Learning: A Review. Preprints, 2018100218.","DOI":"10.20944\/preprints201810.0218.v1"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-Vector Networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_46","unstructured":"Maity, A. (2016). Supervised Classification of RADARSAT-2 Polarimetric Data for Different Land Features. arXiv."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1269","DOI":"10.1007\/s12517-021-07596-0","article-title":"Microlandform Classification Method for Grid DEMs Based on Support Vector Machine","volume":"14","author":"Zhou","year":"2021","journal-title":"Arab. J. Geosci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"170017","DOI":"10.1063\/1.5012452","article-title":"Contribution to Understanding the Post-Mining Landscape\u2014Application of Airborn LiDAR and Historical Maps at the Example from Silesian Upland (Poland)","volume":"1906","author":"Gawior","year":"2017","journal-title":"AIP Conf. Proc."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"104681","DOI":"10.1016\/j.catena.2020.104681","article-title":"Delineation of Groundwater Potential Zones in a Drought-Prone Semi-Arid Region of East India Using GIS and Analytical Hierarchical Process Techniques","volume":"194","author":"Mukherjee","year":"2020","journal-title":"Catena"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., and Lo, W.Y. (2023, January 1\u20136). Segment Anything. Proceedings of the IEEE International Conference on Computer Vision, Paris, France.","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Brusco, D., Belcore, E., and Piras, M. (2023, January 25\u201327). Popillia Japonica Newman Detection Through Remote Sensing and AI Computer Vision. Proceedings of the 2023 IEEE Conference on AgriFood Electronics (CAFE 2023), Torino, Italy.","DOI":"10.1109\/CAFE58535.2023.10291926"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"85","DOI":"10.4995\/var.2021.15329","article-title":"Multiclass Semantic Segmentation for Digitisation of Movable Heritage Using Deep Learning Techniques","volume":"12","author":"Patrucco","year":"2021","journal-title":"Virtual Archaeol. Rev."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"25","DOI":"10.4995\/var.2021.14179","article-title":"Mo.Se.: Mosaic Image Segmentation Based on Deep Cascading Learning","volume":"12","author":"Felicetti","year":"2021","journal-title":"Virtual Archaeol. Rev."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhao, W., Sun, B., Zhang, Y., and Wen, W. (2022). Point Cloud Upsampling Algorithm: A Systematic Review. Algorithms, 15.","DOI":"10.3390\/a15040124"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2013.11.008","article-title":"Multiple-Entity Based Classification of Airborne Laser Scanning Data in Urban Areas","volume":"88","author":"Xu","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1007\/s00138-024-01543-1","article-title":"A Comprehensive Overview of Deep Learning Techniques for 3D Point Cloud Classification and Semantic Segmentation","volume":"35","author":"Sarker","year":"2024","journal-title":"Mach. Vis. Appl."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Matrone, F., Grilli, E., Martini, M., Paolanti, M., Pierdicca, R., and Remondino, F. (2020). Comparing Machine and Deep Learning Methods for Large 3D Heritage Semantic Segmentation. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9090535"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Yang, S., Hou, M., and Li, S. (2023). Three-Dimensional Point Cloud Semantic Segmentation for Cultural Heritage: A Comprehensive Review. Remote Sens., 15.","DOI":"10.3390\/rs15030548"},{"key":"ref_59","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_60","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 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.178"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"43","DOI":"10.5194\/isprs-archives-XLVIII-3-W2-2022-43-2022","article-title":"Robust Techniques for Building Footprint Extraction in Aerial Laser Scanning 3D Point Clouds","volume":"XLVIII-3\/W2-2022","author":"Nurunnabi","year":"2022","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"100001","DOI":"10.1016\/j.ophoto.2021.100001","article-title":"The Hessigheim 3D (H3D) Benchmark on Semantic Segmentation of High-Resolution 3D Point Clouds and Textured Meshes from UAV LiDAR and Multi-View-Stereo","volume":"1","author":"Laupheimer","year":"2021","journal-title":"ISPRS Open J. Photogramm. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"329","DOI":"10.5194\/isprs-archives-XLIII-B2-2022-329-2022","article-title":"A TWO-STAGE APPROACH FOR RARE CLASS SEGMENTATION IN LARGE-SCALE URBAN POINT CLOUDS","volume":"43","author":"Zhang","year":"2022","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci.\u2014ISPRS Arch."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"267","DOI":"10.5194\/isprs-annals-V-2-2022-267-2022","article-title":"Multi-Modal Semantic Mesh Segmentation in Urban Scenes","volume":"5","author":"Laupheimer","year":"2022","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Nong, X., Bai, W., and Liu, G. (2023). Airborne LiDAR Point Cloud Classification Using PointNet++ Network with Full Neighborhood Features. PLoS ONE, 18.","DOI":"10.1371\/journal.pone.0280346"},{"key":"ref_66","unstructured":"Li, Y., Bu, R., Sun, M., Wu, W., Di, X., and Chen, B. (2018). PointCNN: Convolution On X-Transformed Points. arXiv."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Hu, Q., Yang, B., Xie, L., Rosa, S., Guo, Y., Wang, Z., Trigoni, N., and Markham, A. (2019, January 15\u201320). RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR42600.2020.01112"},{"key":"ref_68","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 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA."},{"key":"ref_69","first-page":"8338","article-title":"Learning Semantic Segmentation of Large-Scale Point Clouds with Random Sampling","volume":"44","author":"Hu","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Ballouch, Z., Hajji, R., Poux, F., Kharroubi, A., and Billen, R. (2022). A Prior Level Fusion Approach for the Semantic Segmentation of 3D Point Clouds Using Deep Learning. Remote Sens., 14.","DOI":"10.3390\/rs14143415"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"4338","DOI":"10.1109\/TPAMI.2020.3005434","article-title":"Deep Learning for 3D Point Clouds: A Survey","volume":"43","author":"Guo","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Varney, N., Asari, V.K., and Graehling, Q. (2020, January 13\u201319). DALES: A Large-Scale Aerial LiDAR Data Set for Semantic Segmentation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00101"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1419","DOI":"10.5194\/isprs-archives-XLIII-B2-2020-1419-2020","article-title":"A Benchmark for Large-Scale Heritage Point Cloud Semantic Segmentation","volume":"XLIII-B2-2020","author":"Matrone","year":"2020","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.isprsjprs.2013.11.001","article-title":"Contextual Classification of Lidar Data and Building Object Detection in Urban Areas","volume":"87","author":"Niemeyer","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_75","first-page":"1593","article-title":"Extracting Topographic Structure from Digital Elevation Data for Geographic Information System Analysis","volume":"54","author":"Jenson","year":"1988","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"1705","DOI":"10.1177\/0278364912458814","article-title":"Challenging Data Sets for Point Cloud Registration Algorithms","volume":"31","author":"Pomerleau","year":"2012","journal-title":"Int. J. Robot. Res."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.1080\/01431160512331314083","article-title":"Support Vector Machines for Classification in Remote Sensing","volume":"26","author":"Pal","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.aci.2018.08.003","article-title":"Classification Assessment Methods","volume":"17","author":"Tharwat","year":"2018","journal-title":"Appl. Comput. Inform."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.5194\/isprs-archives-XLVIII-M-2-2023-1189-2023","article-title":"Synthetic Data Generation and Testing for the Semantic Segmentation of Heritage Buildings","volume":"XLVIII-M-2\u20132023","author":"Pellis","year":"2023","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1181","DOI":"10.5194\/isprs-archives-XLVIII-M-2-2023-1181-2023","article-title":"Enhancing Automation of Heritage Processes: Generation of Artificial Training Datasets from Photogrammetric 3D Models","volume":"XLVIII-M-2\u20132023","author":"Patrucco","year":"2023","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Iman, M., Arabnia, H.R., and Rasheed, K. (2023). A Review of Deep Transfer Learning and Recent Advancements. Technologies, 11.","DOI":"10.3390\/technologies11020040"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.aiopen.2021.08.002","article-title":"Pre-Trained Models: Past, Present and Future","volume":"2","author":"Han","year":"2021","journal-title":"AI Open"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/19\/3572\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:02:53Z","timestamp":1760112173000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/19\/3572"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,25]]},"references-count":82,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["rs16193572"],"URL":"https:\/\/doi.org\/10.3390\/rs16193572","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,25]]}}}