{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T18:16:19Z","timestamp":1769883379299,"version":"3.49.0"},"reference-count":34,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,8,25]],"date-time":"2022-08-25T00:00:00Z","timestamp":1661385600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Spanish Ministerio de Ciencia","award":["RTI2018-093874-B-100"],"award-info":[{"award-number":["RTI2018-093874-B-100"]}]},{"name":"Spanish Ministerio de Ciencia","award":["H2019\/HUM-5692"],"award-info":[{"award-number":["H2019\/HUM-5692"]}]},{"name":"Spanish Ministerio de Ciencia","award":["PCD1912570307 AUDECA"],"award-info":[{"award-number":["PCD1912570307 AUDECA"]}]},{"name":"Spanish Ministerio de Ciencia","award":["PCD1912570308 ALVAC"],"award-info":[{"award-number":["PCD1912570308 ALVAC"]}]},{"name":"Innovaci\u00f3n y Universidades research project DEEP-MAPS","award":["RTI2018-093874-B-100"],"award-info":[{"award-number":["RTI2018-093874-B-100"]}]},{"name":"Innovaci\u00f3n y Universidades research project DEEP-MAPS","award":["H2019\/HUM-5692"],"award-info":[{"award-number":["H2019\/HUM-5692"]}]},{"name":"Innovaci\u00f3n y Universidades research project DEEP-MAPS","award":["PCD1912570307 AUDECA"],"award-info":[{"award-number":["PCD1912570307 AUDECA"]}]},{"name":"Innovaci\u00f3n y Universidades research project DEEP-MAPS","award":["PCD1912570308 ALVAC"],"award-info":[{"award-number":["PCD1912570308 ALVAC"]}]},{"DOI":"10.13039\/501100006541","name":"CAM research project LABPA-CM","doi-asserted-by":"publisher","award":["RTI2018-093874-B-100"],"award-info":[{"award-number":["RTI2018-093874-B-100"]}],"id":[{"id":"10.13039\/501100006541","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006541","name":"CAM research project LABPA-CM","doi-asserted-by":"publisher","award":["H2019\/HUM-5692"],"award-info":[{"award-number":["H2019\/HUM-5692"]}],"id":[{"id":"10.13039\/501100006541","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006541","name":"CAM research project LABPA-CM","doi-asserted-by":"publisher","award":["PCD1912570307 AUDECA"],"award-info":[{"award-number":["PCD1912570307 AUDECA"]}],"id":[{"id":"10.13039\/501100006541","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006541","name":"CAM research project LABPA-CM","doi-asserted-by":"publisher","award":["PCD1912570308 ALVAC"],"award-info":[{"award-number":["PCD1912570308 ALVAC"]}],"id":[{"id":"10.13039\/501100006541","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Research Project","award":["RTI2018-093874-B-100"],"award-info":[{"award-number":["RTI2018-093874-B-100"]}]},{"name":"National Research Project","award":["H2019\/HUM-5692"],"award-info":[{"award-number":["H2019\/HUM-5692"]}]},{"name":"National Research Project","award":["PCD1912570307 AUDECA"],"award-info":[{"award-number":["PCD1912570307 AUDECA"]}]},{"name":"National Research Project","award":["PCD1912570308 ALVAC"],"award-info":[{"award-number":["PCD1912570308 ALVAC"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Point clouds are very common tools used in the work of documenting historic heritage buildings. These clouds usually comprise millions of unrelated points and are not presented in an efficient data structure, making them complicated to use. Furthermore, point clouds do not contain topological or semantic information on the elements they represent. Added to these difficulties is the fact that a variety of different kinds of sensors and measurement methods are used in study and documentation work: photogrammetry, LIDAR, etc. Each point cloud must be fused and integrated so that decisions can be taken based on the total information supplied by all the sensors used. A system must be devised to represent the discrete set of points in order to organise, structure and fuse the point clouds. In this work we propose the concept of multispectral voxels to fuse the point clouds, thus integrating multisensor information in an efficient data structure, and applied it to the real case of a building element in an archaeological context. The use of multispectral voxels for the fusion of point clouds integrates all the multisensor information in their structure. This allows the use of very powerful algorithms such as automatic learning and machine learning to interpret the elements studied.<\/jats:p>","DOI":"10.3390\/rs14174172","type":"journal-article","created":{"date-parts":[[2022,8,26]],"date-time":"2022-08-26T02:04:32Z","timestamp":1661479472000},"page":"4172","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Multisensor Data Fusion by Means of Voxelization: Application to a Construction Element of Historic Heritage"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9850-6015","authenticated-orcid":false,"given":"Javier","family":"Raimundo","sequence":"first","affiliation":[{"name":"Departamento de Ingenier\u00eda Topogr\u00e1fica y Cartogr\u00e1fica, Escuela T\u00e9cnica Superior de Ingenieros en Topograf\u00eda, Geodesia y Cartograf\u00eda, Universidad Polit\u00e9cnica de Madrid, Campus Sur, A-3, Km 7, 28031 Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2396-7815","authenticated-orcid":false,"given":"Serafin","family":"Lopez-Cuervo Medina","sequence":"additional","affiliation":[{"name":"Departamento de Ingenier\u00eda Topogr\u00e1fica y Cartogr\u00e1fica, Escuela T\u00e9cnica Superior de Ingenieros en Topograf\u00eda, Geodesia y Cartograf\u00eda, Universidad Polit\u00e9cnica de Madrid, Campus Sur, A-3, Km 7, 28031 Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6430-990X","authenticated-orcid":false,"given":"Julian","family":"Aguirre de Mata","sequence":"additional","affiliation":[{"name":"Departamento de Ingenier\u00eda Topogr\u00e1fica y Cartogr\u00e1fica, Escuela T\u00e9cnica Superior de Ingenieros en Topograf\u00eda, Geodesia y Cartograf\u00eda, Universidad Polit\u00e9cnica de Madrid, Campus Sur, A-3, Km 7, 28031 Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7235-5295","authenticated-orcid":false,"given":"Juan F.","family":"Prieto","sequence":"additional","affiliation":[{"name":"Departamento de Ingenier\u00eda Topogr\u00e1fica y Cartogr\u00e1fica, Escuela T\u00e9cnica Superior de Ingenieros en Topograf\u00eda, Geodesia y Cartograf\u00eda, Universidad Polit\u00e9cnica de Madrid, Campus Sur, A-3, Km 7, 28031 Madrid, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"103675","DOI":"10.1016\/j.autcon.2021.103675","article-title":"Voxel-based representation of 3D point clouds: Methods, applications, and its potential use in the construction industry","volume":"126","author":"Xu","year":"2021","journal-title":"Autom. Constr."},{"key":"ref_2","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_3","doi-asserted-by":"crossref","unstructured":"Poux, F., Neuville, R., Van Wersch, L., Nys, G.A., and Billen, R. (2017). 3D point clouds in archaeology: Advances in acquisition, processing and knowledge integration applied to quasi-planar objects. Geosciences, 7.","DOI":"10.3390\/geosciences7040096"},{"key":"ref_4","unstructured":"Foley, J.D. (1990). Computer Graphics: Principles and Practice, Addison Wesley."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Okhrimenko, M., Coburn, C., and Hopkinson, C. (2019). Multi-spectral lidar: Radiometric calibration, canopy spectral reflectance, and vegetation vertical SVI profiles. Remote Sens., 11.","DOI":"10.3390\/rs11131556"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Goodbody, T.R., Tompalski, P., Coops, N.C., Hopkinson, C., Treitz, P., and van Ewijk, K. (2020). Forest Inventory and Diversity Attribute Modelling Using Structural and Intensity Metrics from Multi-Spectral Airborne Laser Scanning Data. Remote Sens., 12.","DOI":"10.3390\/rs12132109"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Jurado, J.M., Ortega, L., Cubillas, J.J., and Feito, F.R. (2020). Multispectral Mapping on 3D Models and Multi-Temporal Monitoring for Individual Characterization of Olive Trees. Remote Sens., 12.","DOI":"10.3390\/rs12071106"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"18360","DOI":"10.3390\/s150818360","article-title":"From Laser Scanning to Finite Element Analysis of Complex Buildings by Using a Semi-Automatic Procedure","volume":"15","author":"Castellazzi","year":"2015","journal-title":"Sensors"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Li, D., Shen, X., Yu, Y., Guan, H., Li, J., Zhang, G., and Li, D. (2020). Building Extraction from Airborne Multi-Spectral LiDAR Point Clouds Based on Graph Geometric Moments Convolutional Neural Networks. Remote Sens., 12.","DOI":"10.3390\/rs12193186"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"04015015","DOI":"10.1061\/(ASCE)CP.1943-5487.0000480","article-title":"Image-Based 3D Reconstruction for Posthurricane Residential Building Damage Assessment","volume":"30","author":"Zhou","year":"2016","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1016\/j.isprsjprs.2018.08.010","article-title":"A new method for 3D individual tree extraction using multispectral airborne LiDAR point clouds","volume":"144","author":"Dai","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","first-page":"48","article-title":"3D voxel fusion of multi-modality medical images in a clinical treatment planning system","volume":"17","author":"Xie","year":"2004","journal-title":"IEEE Symp. Comput.-Based Med. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.artmed.2019.03.004","article-title":"Reliability-based robust multi-atlas label fusion for brain MRI segmentation","volume":"96","author":"Sun","year":"2019","journal-title":"Artif. Intell. Med."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"102082","DOI":"10.1016\/j.media.2021.102082","article-title":"Deep Fusion of Brain Structure-Function in Mild Cognitive Impairment","volume":"72","author":"Zhang","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_15","first-page":"1170","article-title":"Three-dimensional geological modeling method of regular voxel splitting based on multi-source data fusion","volume":"42","author":"Li","year":"2021","journal-title":"Yantu Lixue Rock Soil Mech."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yang, B., Guo, R., Liang, M., Casas, S., and Urtasun, R. (2020). RadarNet: Exploiting Radar for Robust Perception of Dynamic Objects. arXiv.","DOI":"10.1007\/978-3-030-58523-5_29"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Nobis, F., Shafiei, E., Karle, P., Betz, J., and Lienkamp, M. (2021). Radar Voxel Fusion for 3D Object Detection. Appl. Sci., 11.","DOI":"10.3390\/app11125598"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"98","DOI":"10.3390\/signals2010009","article-title":"3D Object Detection Using Frustums and Attention Modules for Images and Point Clouds","volume":"2","author":"Li","year":"2021","journal-title":"Signals"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"475","DOI":"10.18293\/SEKE2021-115","article-title":"Multi-fusion with attention mechanism for 3D object detection","volume":"2021","author":"Wang","year":"2021","journal-title":"Int. Conf. Softw. Eng. Knowl. Eng. SEKE"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4672","DOI":"10.1109\/LRA.2021.3068712","article-title":"Volumetric Propagation Network: Stereo-LiDAR Fusion for Long-Range Depth Estimation","volume":"6","author":"Choe","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1002\/rse2.182","article-title":"Remote sensing data fusion as a tool for biomass prediction in extensive grasslands invaded by L. polyphyllus","volume":"7","author":"Wachendorf","year":"2021","journal-title":"Remote Sens. Ecol. Conserv."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Yang, C., Li, Y., Wei, M., and Wen, J. (2020, January 25\u201327). Voxel-Based Texture Mapping and 3-D Scene-data Fusion with Radioactive Source. Proceedings of the 2020 The 8th International Conference on Information Technology: IoT and Smart City, Xi\u2019an, China.","DOI":"10.1145\/3446999.3447019"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"170355","DOI":"10.1109\/ACCESS.2020.3024288","article-title":"3D model inpainting based on 3D deep convolutional generative adversarial network","volume":"8","author":"Wang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1111\/j.1477-9730.2006.00379.x","article-title":"Registration of terrestrial laser scanner data using imagery","volume":"21","author":"Fraser","year":"2006","journal-title":"Photogramm. Rec."},{"key":"ref_25","first-page":"181","article-title":"Multispectral photogrammetry: 3D models highlighting traces of paint on ancient sculptures","volume":"2364","author":"Hedeaard","year":"2019","journal-title":"CEUR Workshop Proc."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Raimundo, J., Medina, S.L.C., Prieto, J.F., and de Mata, J.A. (2021). Super resolution infrared thermal imaging using pansharpening algorithms: Quantitative assessment and application to uav thermal imaging. Sensors, 21.","DOI":"10.3390\/s21041265"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"207","DOI":"10.5194\/isprsarchives-XL-1-W4-207-2015","article-title":"Estimation of the spectral sensitivity functions of un-modified and modified commercial off-the-shelf digital cameras to enable their use as a multispectral imaging system for UAVs","volume":"40","author":"Berra","year":"2015","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. ISPRS Arch."},{"key":"ref_28","first-page":"191","article-title":"Automated Voxel model from point clouds for structural analysis of Cultural Heritage","volume":"XLI-B5","author":"Bitelli","year":"2016","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhang, C., Jamshidi, M., Chang, C.C., Liang, X., Chen, Z., and Gui, W. (2022). Concrete Crack Quantification using Voxel-Based Reconstruction and Bayesian Data Fusion. IEEE Trans. Ind. Inform., 1.","DOI":"10.1109\/TII.2022.3147814"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wang, Y., Xiao, Y., Xiong, F., Jiang, W., Cao, Z., Zhou, J.T., and Yuan, J. (2020, January 13\u201319). 3DV: 3D dynamic voxel for action recognition in depth video. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00059"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Poux, F., Neuville, R., Nys, G.A., and Billen, R. (2018). 3D point cloud semantic modelling: Integrated framework for indoor spaces and furniture. Remote Sens., 10.","DOI":"10.3390\/rs10091412"},{"key":"ref_32","unstructured":"Zhou, Q.Y., Park, J., and Koltun, V. (2018). Open3D: A Modern Library for 3D Data Processing. arXiv."},{"key":"ref_33","first-page":"2195","article-title":"Improved ICP point cloud registration based on KDTree","volume":"9","author":"Shi","year":"2016","journal-title":"Int. J. Earth Sci. Eng."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"129","DOI":"10.5194\/isprs-annals-V-2-2021-129-2021","article-title":"Automatic Point Cloud Segmentation for the Detection of Alterations on Historical Buildings Through an Unsupervised and Clustering-Based Machine Learning Approach","volume":"V-2-2021","author":"Musicco","year":"2021","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/17\/4172\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:15:12Z","timestamp":1760141712000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/17\/4172"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,25]]},"references-count":34,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["rs14174172"],"URL":"https:\/\/doi.org\/10.3390\/rs14174172","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,25]]}}}