Vol. 2 No. 2 (2022)

Detection of Road Distress with Mobile Phone LiDAR Sensors

Published 2022-09-30


  • Mobile Phone,
  • LiDAR,
  • Road,
  • Pothole,
  • Rut

How to Cite

Zeybek, M., & Ediz, D. (2022). Detection of Road Distress with Mobile Phone LiDAR Sensors. Advanced LiDAR, 2(2), 48–53. Retrieved from https://publish.mersin.edu.tr/index.php/lidar/article/view/465


Comfort and safety on urban roads are two important and desirable factors in road transport networks. When it comes to providing road comfort, potholes, which emerge on the road surface for different reasons, are one of the problems that we do not want to encounter in our daily transportation. Although different techniques are already being applied to detect deformations on road surfaces, developments in measurement technologies gradually bring alternative techniques with them. The best example for this are the small size LiDAR sensors which have newly been added to mobile phones, and the subject of this study is whether they can be used in detecting such problems. The data collected from the field survey enabled a detailed examination of the road potholes with the proposed methodology based on region growing and plane fitting. According to the results, the 3D sensor technologies will take a new place in measurement technologies by providing high dense data and visualisations in small-sized projects, thus facilitating instant decisions. In the study, potholes on the road surface were determined with 4 different data sets obtained in Denizli province Pamukkale district Karahayıt neighbourhood and detailed information about their characteristics was collected. As a result, with this study, the LiDAR sensor released to the market by Apple on iPhone 12 and following versions has developed an alternative measurement technique and methodology that can be used in the implementation of small-sized projects on road surfaces. It is clear that the use of mobile phone-based LiDAR sensors in road repair services has significant potential in the near future.


  1. Aslan, İ. & Polat, N. (2022). Availability of Iphone 13 Pro Laser Data in 3D Modeling. Advanced LiDAR, 2(1).
  2. Chang, Y. L. & Li, X. (1994). Adaptive image region-growing. IEEE Trans Image Process, 3(6), 868-872. doi:10.1109/83.336259
  3. Findley, D. J., Cunningham, C. M., Thomas H. Brown, J., Cahill, L. M., Yang, G. & Huntsinger, L. F. (2022). Highway Engineering Planning, Design, and Operations.
  4. George B. Thomas, J., Weir, M. D., Hass, J., Heil, C. & Behn, A. (2016). THOMAS’ CALCULUS Thirteenth Edition in SI Units.
  5. Gollob, C., Ritter, T., Kraßnitzer, R., Tockner, A. & Nothdurft, A. (2021). Measurement of Forest Inventory Parameters with Apple iPad Pro and Integrated LiDAR Technology. Remote Sensing, 13(16). doi:10.3390/rs13163129
  6. Islam, M. R. & Tarefder, R. A. (2020). Pavement Design Materials, Analysis, and Highways: McGraw Hill.
  7. Kuçak, R. A., Erol, S. & Alkan, R. M. (2023). iPad Pro LiDAR sensörünün profesyonel bir yersel lazer tarayıcı ile karşılaştırmalı performans analizi Comparative performance analysis of the iPad Pro LiDAR sensor with a professional terrestrial laser scanner. Geomatik Dergisi, 8(1). doi:10.29128/geomatik.1105048
  8. Li, X. N., Ling, S. X., Sun, C. W., Xu, J. X., & Huang, T. (2019). Integrated rockfall hazard and risk assessment along highways: An example for Jiuzhaigou area after the 2017 Ms 7.0 Jiuzhaigou earthquake, China. Journal of Mountain Science, 16(6), 1318-1335. doi:10.1007/s11629-018-5355-x
  9. Ooms, J. (2021). magick: Advanced Graphics and Image-Processing in R (Version R package version 2.7.3). Retrieved from https://CRAN.R-project.org/package=magick
  10. Rabbani, T., Van Den Heuvel, F., & Vosselmann, G. (2006). Segmentation of point clouds using smoothness constraint. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 36(5), 248-253.
  11. Strutz, T. (2011). Data Fitting and Uncertainty: Springer Vieweg.
  12. Team, R. C. (2021). R: A Language and Environment for Statistical Computing. Retrieved from https://cran.r-project.org/
  13. Vo, A.-V., Truong-Hong, L., Laefer, D. F., & Bertolotto, M. (2015). Octree-based region growing for point cloud segmentation. ISPRS Journal of Photogrammetry and Remote Sensing, 104, 88-100. doi:10.1016/j.isprsjprs.2015.01.011
  14. Vosselman, G., & Maas, H.-G. (2010). Airborne and Terrestrial Laser Scanning: Whittles Publishing.
  15. Wang, F., Heenkenda, M. K., & Freeburn, J. T. (2022). Estimating tree Diameter at Breast Height (DBH) using an iPad Pro LiDAR sensor. Remote Sensing Letters, 13(6), 568-578. doi:10.1080/2150704x.2022.2051635
  16. Wei, C. T., Tsai, M. D., Chang, Y. L., & Wang, M. C. J. (2022). Enhancing the Accuracy of Land Cover Classification by Airborne LiDAR Data and WorldView-2 Satellite Imagery. ISPRS International Journal of Geo-Information, 11(7). Retrieved from ://WOS:000834409700001
  17. Yılmaz, H. M., & Yakar, M. (2008). Lidar (Light Detection And Ranging) Tarama Sistemi. Yapı Teknolojileri Elektronik Dergisi, 2, 23-33.