{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T06:58:33Z","timestamp":1776063513326,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,4,29]],"date-time":"2023-04-29T00:00:00Z","timestamp":1682726400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006360","name":"German Federal Ministry for Economic Affairs and Energy (BMWi)","doi-asserted-by":"publisher","award":["16KN086442"],"award-info":[{"award-number":["16KN086442"]}],"id":[{"id":"10.13039\/501100006360","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Terrestrial laser scanners (TLSs) are a standard method for 3D point cloud acquisition due to their high data rates and resolutions. In certain applications, such as deformation analysis, modelling uncertainties in the 3D point cloud is crucial. This study models the systematic deviations in laser scan distance measurements as a function of various influencing factors using machine-learning methods. A reference point cloud is recorded using a laser tracker (Leica AT 960) and a handheld scanner (Leica LAS-XL) to investigate the uncertainties of the Z+F Imager 5016 in laboratory conditions. From 49 TLS scans, a wide range of data are obtained, covering various influencing factors. The processes of data preparation, feature engineering, validation, regression, prediction, and result analysis are presented. The results of traditional machine-learning methods (multiple linear and nonlinear regression) are compared with eXtreme gradient boosted trees (XGBoost). Thereby, it is demonstrated that it is possible to model the systemic deviations of the distance measurement with a coefficient of determination of 0.73, making it possible to calibrate the distance measurement to improve the laser scan measurement. An independent TLS scan is used to demonstrate the calibration results.<\/jats:p>","DOI":"10.3390\/rs15092349","type":"journal-article","created":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T12:10:03Z","timestamp":1682943003000},"page":"2349","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Uncertainty Modelling of Laser Scanning Point Clouds Using Machine-Learning Methods"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6329-2061","authenticated-orcid":false,"given":"Jan","family":"Hartmann","sequence":"first","affiliation":[{"name":"Geodetic Institute, Leibniz Universit\u00e4t Hannover, Nienburger Str. 1, 30167 Hannover, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4480-1067","authenticated-orcid":false,"given":"Hamza","family":"Alkhatib","sequence":"additional","affiliation":[{"name":"Geodetic Institute, Leibniz Universit\u00e4t Hannover, Nienburger Str. 1, 30167 Hannover, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,29]]},"reference":[{"key":"ref_1","unstructured":"Joint Committee for Guides in Metrology (2023, March 09). Evaluation of Measurement Data\u2014Guide to the Expression of Uncertainty in Measurement. Available online: https:\/\/www.iso.org\/sites\/JCGM\/GUM-JCGM100.htm."},{"key":"ref_2","first-page":"67","article-title":"Uncertainty modeling of random and systematic errors by means of Monte Carlo and fuzzy techniques","volume":"3","author":"Alkhatib","year":"2009","journal-title":"J. Appl. Geod."},{"key":"ref_3","first-page":"125","article-title":"Estimation of Measurement Uncertainty of kinematic TLS Observation Process by means of Monte-Carlo Methods","volume":"7","author":"Alkhatib","year":"2013","journal-title":"J. Appl. Geod."},{"key":"ref_4","unstructured":"Neitzel, F. (2006). Terrestrisches Laser-Scanning (TLS 2006), Schriftenreihe des DVW, Band 51, Wi\u00dfner-Verlag."},{"key":"ref_5","unstructured":"Neitzel, F. (2006). Photogrammetrie-Laserscanning-Optische 3D-Messtechnik, Beitr\u00e4ge der Oldenburger 3D-Tage, Herbert Wichmann Verlag."},{"key":"ref_6","first-page":"17","article-title":"Challenges and present fields of action at laser scanner based deformation analyses","volume":"2016","author":"Holst","year":"2016","journal-title":"J. Appl. Geod."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1515\/jag-2018-0032","article-title":"Empirical stochastic model of detected target centroids: Influence on registration and calibration of terrestrial laser scanners","volume":"13","author":"Holst","year":"2019","journal-title":"J. Appl. Geod."},{"key":"ref_8","unstructured":"Medi\u0107, T., Holst, C., and Kuhlmann, H. (2020). Allgemeine Vermessungs-Nachrichten: AVN, Zeitschrift f\u00fcr alle Bereiche der Geod\u00e4sie und Geoinformation; VDE Verlag."},{"key":"ref_9","first-page":"139","article-title":"Volumetric performance evaluation of a laser scanner based on geometric error model","volume":"40","author":"Muralikrishnan","year":"2015","journal-title":"Precis. Eng.-J. Int. Soc. Precis. Eng. Nanotechnol."},{"key":"ref_10","unstructured":"Gordon, B. (2008). Zur Bestimmung von Messunsicherheiten Terrestrischer Laserscanner. [Ph.D. Thesis, Technische Universit\u00e4t Darmstadt]."},{"key":"ref_11","unstructured":"Juretzko, M. (2004). Reflektorlose Video-Tachymetrie: Ein Integrales Verfahren zur Erfassung Geometrischer und Visueller Informationen. [Ph.D. Thesis, Ruhr-Universit\u00e4t Bochum]."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1016\/j.isprsjprs.2011.01.005","article-title":"Scanning geometry: Influencing factor on the quality of terrestrial laser scanning points","volume":"66","author":"Soudarissanane","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_13","unstructured":"Z\u00e1mevcn\u00edkov\u00e1, M. (2017). FIG Working Week 2017, FIG."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Kop\u00e1\u010dik, A., Kyrinovi\u010d, P., Erd\u00e9lyi, J., Paar, R., and Marendi\u0107, A. (2021). Contributions to International Conferences on Engineering Surveying, Springer. Springer Proceedings in Earth and Environmental Sciences.","DOI":"10.1007\/978-3-030-51953-7"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.isprsjprs.2016.12.006","article-title":"An intensity-based stochastic model for terrestrial laser scanners","volume":"125","author":"Wujanz","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1515\/jag-2016-0026","article-title":"A synthetic covariance matrix for monitoring by terrestrial laser scanning","volume":"11","author":"Kauker","year":"2017","journal-title":"J. Appl. Geod."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1515\/jag-2019-0002","article-title":"Influence of the simplified stochastic model of TLS measurements on geometry-based deformation analysis","volume":"13","author":"Zhao","year":"2019","journal-title":"J. Appl. Geod."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Schmitz, B., Holst, C., Medic, T., Lichti, D.D., and Kuhlmann, H. (2019). How to Efficiently Determine the Range Precision of 3D Terrestrial Laser Scanners. Sensors, 19.","DOI":"10.3390\/s19061466"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kermarrec, G., Alkhatib, H., and Neumann, I. (2018). On the Sensitivity of the Parameters of the Intensity-Based Stochastic Model for Terrestrial Laser Scanner. Case Study: B-Spline Approximation. Sensors, 18.","DOI":"10.3390\/s18092964"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Stenz, U., Hartmann, J., Paffenholz, J.A., and Neumann, I. (2020). High-Precision 3D Object Capturing with Static and Kinematic Terrestrial Laser Scanning in Industrial Applications\u2014Approaches of Quality Assessment. Remote Sens., 12.","DOI":"10.3390\/rs12020290"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Stenz, U., Hartmann, J., Paffenholz, J.A., and Neumann, I. (2017). A Framework Based on Reference Data with Superordinate Accuracy for the Quality Analysis of Terrestrial Laser Scanning-Based Multi-Sensor-Systems. Sensors, 17.","DOI":"10.3390\/s17081886"},{"key":"ref_22","unstructured":"Hastie, T.J., Friedman, J.H., and Tibshirani, R. (2017). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer. [2nd ed.]."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Hartmann, J., Heiken, M., Alkhatib, H., and Neumann, I. (2023). Automatic quality assessment of terrestrial laser scans. J. Appl. Geod.","DOI":"10.1515\/jag-2022-0030"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"065201","DOI":"10.1088\/1361-6501\/abd57a","article-title":"Machine learning method for predicting the influence of scanning parameters on random measurement error","volume":"32","author":"Urbas","year":"2021","journal-title":"Meas. Sci. Technol."},{"key":"ref_25","unstructured":"Hexagon Manufacturing Intelligence (2023, March 17). Leica Absolute Tracker AT960 Datasheet 2023. Available online: https:\/\/hexagon.com\/de\/products\/leica-absolute-tracker-at960?accordId=E4BF01077B2743729F2C0E768C0BC7AB."},{"key":"ref_26","unstructured":"Hexagon Manufacturing Intelligence (2023, March 09). Leica-Laser Tracker Systems. Available online: https:\/\/www.hexagonmi.com\/de-de\/products\/laser-tracker-systems."},{"key":"ref_27","unstructured":"(2023, March 09). Zoller + Fr\u00f6hlich GmbH. Z+F IMAGER\u00ae Z+F IMAGER 5016: Data Sheet. Available online: https:\/\/scandric.de\/wp-content\/uploads\/ZF-IMAGER-5016_Datenblatt-D_kompr.pdf."},{"key":"ref_28","unstructured":"technet GmbH (2023, March 09). Scantra, Version 3.0.1. Available online: https:\/\/www.technet-gmbh.com\/produkte\/scantra\/."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1111\/cgf.14077","article-title":"Poisson Surface Reconstruction with Envelope Constraints","volume":"39","author":"Kazhdan","year":"2020","journal-title":"Comput. Graph. Forum"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1007\/s10846-014-0155-1","article-title":"An Extended Evaluation of Open Source Surface Reconstruction Software for Robotic Applications","volume":"77","author":"Wiemann","year":"2015","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_31","unstructured":"Zhou, Q.Y., Park, J., and Koltun, V. (2018). Open3D: A Modern Library for 3D Data Processing. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1109\/LGRS.2008.916978","article-title":"Quantifying the Size of a Lidar Footprint: A Set of Generalized Equations","volume":"5","author":"Sheng","year":"2008","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Hackel, T., Wegner, J., 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 2016), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.178"},{"key":"ref_34","unstructured":"Koch, K.R. (1997). Parametersch\u00e4tzung und Hypothesentests in Linearen Modellen, D\u00fcmmler."},{"key":"ref_35","unstructured":"Krishnapuram, B., Shah, M., Smola, A., Aggarwal, C., Shen, D., and Rastogi, R. (2016, January 13\u201317). XGBoost. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA."},{"key":"ref_36","unstructured":"Xgboost developers (2023, March 09). XGboost Parameter Documentation. Available online: https:\/\/xgboost.readthedocs.io\/en\/stable\/parameter.html."},{"key":"ref_37","unstructured":"Bergstra, J., Yamins, D., and Cox, D. (2013, January 16\u201321). Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures. Proceedings of the International Conference on Machine Learning, Atlanta, GA, USA."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/9\/2349\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:26:23Z","timestamp":1760124383000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/9\/2349"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,29]]},"references-count":37,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["rs15092349"],"URL":"https:\/\/doi.org\/10.3390\/rs15092349","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,29]]}}}