Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Journal of Applied Geodesy

Editor-in-Chief: Kahmen, Heribert / Rizos, Chris

CiteScore 2018: 1.61

SCImago Journal Rank (SJR) 2018: 0.532
Source Normalized Impact per Paper (SNIP) 2018: 1.064

See all formats and pricing
More options …
Ahead of print


On the applicability of a scan-based mobile mapping system for monitoring the planarity and subsidence of road surfaces – Pilot study on the A44n motorway in Germany

Erik Heinz
  • Corresponding author
  • Institute of Geodesy and Geoinformation, University of Bonn, Nußallee 17, 53115 Bonn, Germany
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Christian Eling / Lasse Klingbeil / Heiner Kuhlmann
Published Online: 2019-08-30 | DOI: https://doi.org/10.1515/jag-2019-0016


Kinematic laser scanning is widely used for the fast and accurate acquisition of road corridors. In this context, road monitoring is a crucial application, since deficiencies of the road surface due to non-planarity and subsidence put traffic at risk. In recent years, a Mobile Mapping System (MMS) has been developed at the University of Bonn, consisting of a GNSS/IMU unit and a 2D laser scanner. The goal of this paper is to evaluate the accuracy and precision of this MMS, where the height component is of main interest. Following this, the applicability of the MMS for monitoring the planarity and subsidence of road surfaces is analyzed. The test area for this study is a 6 km long section of the A44n motorway in Germany. For the evaluation of the MMS, leveled control points along the motorway as well as point cloud comparisons of repeated passes were used. In order to transform the ellipsoidal heights of the MMS into the physical height system of the control points, undulations were utilized. In this respect, a local tilt correction for the geoid model was determined based on GNSS baselines and leveling, leading to a physical height accuracy of the MMS of < 10 mm (RMS). The related height precision has a standard deviation of about 5 mm. Hence, a potential subsidence of the road surface in the order of a few cm is detectable. In addition, the point clouds were used to analyze the planarity of the road surface. In the course of this, the cross fall of the road was estimated with a standard deviation of < 0.07 %. Yet, no deficiencies of the road surface in the form of significant rut depths or fictive water depths were detected, indicating the proper condition of the A44n motorway. According to our tests, the MMS is appropriate for road monitoring.

Keywords: Kinematic Laser Scanning; Mobile Mapping; Evaluation; Monitoring; Road Surface; Subsidence; Planarity; Road Parameters


  • [1]

    Ampatzidis, D., Bitharis, S., Pikridas, C. and Demirtzoglou, N., On the improvement of the orthometric heights via GNSS-levelling: The case of Drama area in Greece, zfv - Zeitschrift für Geodäsie, Geoinformation und Landmanagement 143 (2018), 185–190.Google Scholar

  • [2]

    Barber, D., Mills, J. and Smith-Voysey, S., Geometric validation of a ground-based mobile laser scanning system, ISPRS Journal of Photogrammetry and Remote Sensing 63 (2008), 128–141.CrossrefGoogle Scholar

  • [3]

    Bundesamt für Kartographie und Geodäsie (BKG), Quasigeoid der Bundesrepublik Deutschland - GCG2016 (German Combined QuasiGeoid 2016), Status: 18.09.2017, Report, 2017.

  • [4]

    Che, E., Jung, J. and Olsen, M. J., Object Recognition, Segmentation, and Classification of Mobile Laser Scanning Point Clouds: A State of the Art Review, Sensors 19 (2019).Google Scholar

  • [5]

    Cloud Compare, 3D Point Cloud and Mesh Processing Software - Open Source Project, Available online: https://www.danielgm.net/cc/ (accessed on 15 March 2019), Report, 2019.Google Scholar

  • [6]

    Eling, C., Heinz, E. and Kuhlmann, H., Vergleich von GNSS-Höhenübertragung und Stromübergangsnivellement am Rhein, zfv - Zeitschrift für Geodäsie, Geoinformation und Landmanagement 139 (2014), 381–388.Google Scholar

  • [7]

    European Commission, EU Transport in figures - Statistical Pocketbook 2018, Luxembourg: Publications Office of the European Union, Report, 2018.

  • [8]

    Fischler, M. A. and Bolles, R. C., Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography, Communications of the ACM 24 (1981), 381–395.CrossrefGoogle Scholar

  • [9]

    Forschungsgesellschaft für das Straßen- und Verkehrswesen (FGSV), Zusätzliche Technische Vertragsbedingungen und Richtlinien zur Zustandserfassung und -bewertung von Straßen (ZTV ZEB-StB), Report, 2006.

  • [10]

    Forschungsgesellschaft für das Straßen- und Verkehrswesen (FGSV), Richtlinien für die Anlage von Autobahnen - RAA, Report, 2008.

  • [11]

    Forschungsgesellschaft für das Straßen- und Verkehrswesen (FGSV), Technische Prüfvorschriften für Ebenheitsmessungen auf Fahrbahnoberflächen in Längs- und Querrichtung (TP Eben), Teil: Berührungslose Messungen, 404/2, Report, 2009.

  • [12]

    Forschungsgesellschaft für das Straßen- und Verkehrswesen (FGSV), Technische Prüfvorschriften für Ebenheitsmessungen auf Fahrbahnoberflächen in Längs- und Querrichtung (TP Eben), Teil: Berührende Messungen, 404/1, Report, 2017.

  • [13]

    Gräfe, G., High precision kinematic surveying with laser scanners, Journal of Applied Geodesy 1 (2007), 185–199.Google Scholar

  • [14]

    Guan, H., Li, J., Cao, S. and Yu, Y., Use of mobile LiDAR in road information inventory, International Journal of Image and Data Fusion 7 (2016), 219–242.CrossrefGoogle Scholar

  • [15]

    Guan, H., Li, J., Yu, Y., Wang, C., Chapman, M. and Yang, B., Using mobile laser scanning data for automated extraction of road markings, ISPRS Journal of Photogrammetry and Remote Sensing 87 (2014), 93–107.CrossrefGoogle Scholar

  • [16]

    Hartmann, J., Trusheim, P., Alkhatib, H., Paffenholz, J.-A., Diener, D. and Neumann, I., High Accurate Pointwise (Geo-)Referencing of a k-TLS based Multi-Sensor-System, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-4, 2018, ISPRS TC IV Mid-term Symposium, Delft, The Netherlands (2018), 81–88.Google Scholar

  • [17]

    Heinz, E., Eling, C., Wieland, M., Klingbeil, L. and Kuhlmann, H., Development, Calibration and Evaluation of a Portable and Direct Georeferenced Laser Scanning System for Kinematic 3D Mapping, Journal of Applied Geodesy 9 (2015), 227–243.Google Scholar

  • [18]

    Heinz, E., Eling, C., Wieland, M., Klingbeil, L. and Kuhlmann, H., Analysis of Different Reference Plane Setups for the Calibration of a Mobile Laser Scanning System, In: Lienhart, W. (Hrsg.): Ingenieurvermessung 17, Beiträge zum 18. Internationalen Ingenieurvermessungskurs, Graz, Österreich, S. 131–145, Wichmann Verlag, Berlin, Offenbach (2017).Google Scholar

  • [19]

    Heinz, E., Mettenleiter, M., Kuhlmann, H. and Holst, C., Strategy for Determining the Stochastic Distance Characteristics of the 2D Laser Scanner Z+F Profiler 9012A with Special Focus on the Close Range, Sensors 18 (2018).Google Scholar

  • [20]

    Holgado-Barco, A., Gonzalez-Aguilera, D., Arias-Sanchez, P., Martinez-Sanchez, J., An automated approach to vertical road characterisation using mobile LiDAR systems: Longitudinal profiles and cross-section, ISPRS Journal of Photogrammetry and Remote Sensing 96 (2014), 28–37.CrossrefGoogle Scholar

  • [21]

    Holgado-Barco, A., González-Aguilera, D., Arias-Sanchez, P., Martinez-Sanchez, J., Semiautomatic Extraction of Road Horizontal Alignment from a Mobile LiDAR System, Computer-Aided Civil and Infrastructure Engineering 30 (2015), 217–228.CrossrefGoogle Scholar

  • [22]

    Holgado-Barco, A., Riveiro, B., González-Aguilera, D., Arias, P., Automatic Inventory of Road Cross-Sections from Mobile Laser Scanning System, Computer-Aided Civil and Infrastructure Engineering 32 (2017), 3–17.CrossrefGoogle Scholar

  • [23]

    IMAR Navigation GmbH, Inertial Navigation System iNAV-FJI-LSURV, Available online: http://www.imar.de/index.php/en/products/by-product-names (accessed on 23 December 2016), Report, 2016.Google Scholar

  • [24]

    Jung, J., Che, E., Olsen, M. J. and Parrish, C., Efficient and robust lane marking extraction from mobile lidar point clouds, ISPRS Journal of Photogrammetry and Remote Sensing 147 (2019), 1–18.CrossrefGoogle Scholar

  • [25]

    Kaartinen, H., Hyyppä, J., Kukko, A., Jaakkola, A. and Hyyppä, H., Benchmarking the Performance of Mobile Laser Scanning Systems Using a Permanent Test Field, Sensors 12 (2012), 12814–12835.CrossrefGoogle Scholar

  • [26]

    Kalenjuk, S., Rebhan, M. J., Lienhart, W., Marte, R., Large-scale monitoring of retaining structures: new approaches on the safety assessment of retaining structures using mobile mapping, Proceedings SPIE, Sensors and Smart Structures Technologies for Civil, Mechanical and Aerospace Systems 2019, Vol. 10970, International Society for Optics and Photonics.Google Scholar

  • [27]

    Kang, Z., Yang, J., Zhong, R., Wu, Y., Shi, Z. and Lindenbergh, R., Voxel-Based Extraction and Classification of 3-D Pole-Like Objects From Mobile LiDAR Point Cloud Data, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11 (2018), 4287–4298.CrossrefGoogle Scholar

  • [28]

    Kremer, J. and Grimm, A., A Dedicated Mobile LIDAR Mapping System For Railway Networks, The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences Vol. XXXIX-B5, 2012, XXII ISPRS Congress, Melbourne, Australia (2012).Google Scholar

  • [29]

    Kuhlmann, H., Schwieger, V., Wieser, A. and Niemeier, W., Engineering Geodesy - Definition and Core Competencies, Journal of Applied Geodesy 8 (2014), 327–333.Google Scholar

  • [30]

    Kukko, A., Kaartinen, H., Hyyppä, J. and Chen, Y., Multiplatform Mobile Laser Scanning: Usability and Performance, Sensors 12 (2012), 11712–11733.CrossrefGoogle Scholar

  • [31]

    Kumar, P., Lewis, P., McElhinney, C. P., Parametric Analysis for Automated Extraction of Road Edges From Mobile Laser Scanning Data, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-2/W2, 2015, Joint International Geoinformation Conference 2015, 28-30 October 2015, Kuala Lumpur, Malaysia.Google Scholar

  • [32]

    Lantieri, C., Lamperti, R., Simone, A., Vignali, V., Sangiorgi, C. and Dondi, G., Mobile Laser Scanning for Assessment of the Rainwater Runoff and Drainage Condition on Road Pavements, International Journal of Pavement Research and Technology 8 (2015), 1–9.Google Scholar

  • [33]

    Lehtomäki, M., Jaakkola, A., Hyyppä, J., Lampinen, J., Kaartinen, H., Kukko, A., Puttonen, E. and Hyyppä, H., Object Classification and Recognition From Mobile Laser Scanning Point Clouds in a Road Environment, IEEE Transactions on Geoscience and Remote Sensing (2015), 1–14.Google Scholar

  • [34]

    Li, F., Elberink, S. O. and Vosselman, G., Pole-Like Road Furniture Detection and Decomposition in Mobile Laser Scanning Data Based on Spatial Relations, Remote Sensing 18 (2018).Google Scholar

  • [35]

    Li, F., Lehtomäki, M., Elberink, S. O., Vosselman, G., Kukko, A., Puttonen, E., Chen, Y., Hyyppä, J., Semantic segmentation of road furniture in mobile laser scanning data, ISPRS Journal of Photogrammetry and Remote Sensing 154 (2019), 98–113.CrossrefGoogle Scholar

  • [36]

    Li, Y., Wang, W., Tang, S., Li, D., Wang, Y., Yuan, Z., Guo, R., Li, X., Xiu, W., Localization and Extraction of Road Poles in Urban Areas from Mobile Laser Scanning Data, Remote Sensing, 11 (2019), 401.CrossrefGoogle Scholar

  • [37]

    Lienhart, W., Kalenjuk, S., Ehrhart, C., Efficient and Large Scale Monitoring of Retaining Walls along Highways using a Mobile Mapping System, 8th International Conference on Structural Health Monitoring of Intelligent Infrastructure, Brisbane, Australia, 5–8 December 2017.

  • [38]

    Ma, L., Li, Y., Li, J., Wang, C., Wang, R. and Chapman, M. A., Mobile Laser Scanned Point-Clouds for Road Object Detection and Extraction: A Review, Remote Sensing 18 (2018).Google Scholar

  • [39]

    Martín-Jiménez, J. A., Zazo, S., Justel, J. J. A., Rodríguez-Gonzálvez, P., González-Aguilera, D., Road safety evaluation through automatic extraction of road horizontal alignments from Mobile LiDAR System and inductive reasoning based on a decision tree, ISPRS Journal of Photogrammetry and Remote Sensing 146 (2018), 334–346.CrossrefGoogle Scholar

  • [40]

    Mikrut, S., Kohut, P., Pyka, K., Tokarczyk, R., Barszcz, T. and Uhl, T., Mobile Laser Scanning Systems for Measuring the Clearance Gauge of Railways: State of Play, Testing and Outlook, Sensors 16 (2016).Google Scholar

  • [41]

    Miraliakbari, A., Hahn, M. and Maas, H.-G., Development of a Multi-Sensor System for Road Condition Mapping, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-1, 2014, ISPRS Technical Commission I Symposium, Denver, Colorado, USA (2014), 265–272.Google Scholar

  • [42]

    Olsen, M. J., Roe, G. V., Glennie, C., Persi, F., Reedy, M., Hurwitz, D., Williams, K., Tuss, H., Squellati, A. and Knodler, M., Guidelines for the Use of Mobile LIDAR in Transportation Applications, National Cooperative Highway Research Program (NCHRP) Report 748, National Academy of Sciences, Washington, D. C., Report, 2013.

  • [43]

    Puente, I., Akinci, B., González-Jorge, H., Díaz-Vilariño, L., Arias, P., A semi-automated method for extracting vertical clearance and cross sections in tunnels using mobile LiDAR data, Tunnelling and Underground Space Technology 59 (2016), 48–54.CrossrefGoogle Scholar

  • [44]

    Reiterer, A., Dambacher, M., Maindorfer, I., Höfler, H., Ebersbach, D., Frey, C., Scheller, S. and Klose, D., Straßenzustandsüberwachung in Submillimeter, In: Photogrammetrie Laserscanning Optische 3D Messtechnik, Beiträge der Oldenburger 3D-Tage 2013, Wichmann Verlag (2013), 78–85.Google Scholar

  • [45]

    Riecken, J. and Kurtenbach, E., Der Satellitenpositionierungsdienst der deutschen Landesvermessung - SAPOS, Zeitschrift für Geodäsie, Geoinformation und Landmanagement (ZfV) 142 (2017), 293–300.Google Scholar

  • [46]

    Schlichting, A., Brenner, C. and Schön, S., Bewertung von Inertial/GNSS-Modulen mittels Laserscannern und bekannter Landmarken, Photogrammetrie, Fernerkundung, Geoinformation (PFG) 2014 (2014), 5–15.CrossrefGoogle Scholar

  • [47]

    Teunissen, P. J. G. and Montenbruck, O. (eds.), Springer Handbook of Global Navigation Satellite Systems, Springer International Publishing, 2017.Google Scholar

  • [48]

    Toschi, I., Rodríguez-Gonzálvez, P., Remondino, F., Minto, S., Orlandini, S. and Fuller, A., Accuracy evaluation of a mobile mapping system with advanced statistical methods, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-5/W4, 2015, Avila, Spain (2015).Google Scholar

  • [49]

    Tucci, G., Visintini, D., Bonora, V. and Parisi, E. I., Examination of Indoor Mobile Mapping Systems in a Diversified Internal/External Test Field, Applied Science 2018 (2018), 401.Google Scholar

  • [50]

    Vaaja, M. T., Kurkela, M., Maksimainen, M., Virtanen, J.-P., Kukko, A., Lehtola, V. V., Hyyppä, J. and Hyyppä, H., Mobile Mapping of Night-Time Road Environment Lighting Conditions, The Photogrammetric Journal of Finland 26 (2018), 1–17.CrossrefGoogle Scholar

  • [51]

    van der Horst, B. B., Lindenbergh, R. C. and Puister, S. W. J., Mobile Laser Scan Data for Road Surface Damage Detection, Proceedings of the ISPRS Geospatial Week 2019, Enschede, The Netherlands, Commission II, WG II/10 (2019).Google Scholar

  • [52]

    Vennegeerts, H., Martin, J., Becker, M. and Kutterer, H., Validation of a kinematic laserscanning system, Journal of Applied Geodesy 2 (2008), 79–84.Google Scholar

  • [53]

    Vittuari, L., Tini, M. A., Sarti, P., Serantoni, E., Borghi, A., Negusini, M. and Guillaume, S., A Comparative Study of the Applied Methods for Estimating Deflection of the Vertical in Terrestrial Geodetic Measurements, Sensors 16 (2016).Google Scholar

  • [54]

    Wang, J., Hu, Z., Chen, Y. and Zhang, Z., Automatic Estimation of Road Slopes and Superelevations Using Point Clouds, Photogrammetric Engineering and Remote Sensing 83 (2017), 217–223.CrossrefGoogle Scholar

  • [55]

    Williams, K., Olsen, M. J., Gene V. R. and Glennie, C., Synthesis of Transportation Applications of Mobile LIDAR, Remote Sensing 2013 (2013), 4652–4692.Google Scholar

  • [56]

    Wen, C., Sun, X., Li, J., Wang, C., Guo, Y., Habib, A., A deep learning framework for road marking extraction, classification and completion from mobile laser scanning point clouds, ISPRS Journal of Photogrammetry and Remote Sensing 147 (2019), 178–192.CrossrefGoogle Scholar

  • [57]

    Zoller & Fröhlich GmbH, Z + F Profiler 9012A, 2D Laser Scanner, Available online: http://www.zflaser.com (accessed on 26 February 2018), Report, 2018.Google Scholar

About the article

Received: 2019-04-27

Accepted: 2019-08-07

Published Online: 2019-08-30

Funding Source: Deutsche Forschungsgemeinschaft

Award identifier / Grant number: FOR 1505

This work was funded by the DFG (Deutsche Forschungsgemeinschaft) under the project number FOR 1505 Mapping on Demand. The authors wish to express their gratitude for this support.

Citation Information: Journal of Applied Geodesy, ISSN (Online) 1862-9024, ISSN (Print) 1862-9016, DOI: https://doi.org/10.1515/jag-2019-0016.

Export Citation

© 2019 Walter de Gruyter GmbH, Berlin/Boston.Get Permission

Comments (0)

Please log in or register to comment.
Log in