Progress on road pavement condition detection based on remote sensing monitoring

  • role: First author第一作者
  • Affiliation:

    Institute of Remote Sensing and Geographic Information System, Peking University, Beijing 100871, China

  • Email:yfpan@pku.edu.cn
  • Introduction:潘一凡(1989— ),男,博士研究生,研究方向为高光谱遥感、机器学习及LiDAR应用。E-mail:yfpan@pku.edu.cn
PAN Yifan1,  
  • role: Corresponding author通信作者
  • Affiliation:

    Institute of Remote Sensing and Geographic Information System, Peking University, Beijing 100871, China

  • Email:xfzhang@pku.edu.cn
  • Introduction:张显峰(1967— ),男,副教授,研究方向为生态遥感、高光谱遥感应用。E-mail:xfzhang@pku.edu.cn
ZHANG Xianfeng1*,  
  • Affiliation:

    Institute of Remote Sensing and Geographic Information System, Peking University, Beijing 100871, China

    Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China

TONG Qingxi12,  
  • Affiliation:

    Institute of Remote Sensing and Geographic Information System, Peking University, Beijing 100871, China

SUN Min1,  
  • Affiliation:

    Remote Sening Center for Transportation, China Transport Telecommunications & Information Center, Beijing 100011, China

LUO Lun3

ملخص

Roads are greatly essential in a transportation system. The quality of road pavements has a crucial impact on the driving safety, comfort, and the cost of the roads. Therefore, timely monitoring the pavement conditions to guarantee the secure operation of the traffic system is significant. Currently, time-consuming field investigations and manual measurements are the conventional and main methods to detect and evaluate the pavement conditions. However, many of these methods are destructive to the road surface. Many forms of remote sensing data without destructive effect on pavement were introduced with the support of computer technology and remote sensing to detect pavement conditions, such as digital images, LiDAR, and Radar. This paper reviewed the current research status and problems in pavement condition detection based on remote sensing technologies. Remote sensing, as a non-intrusive method, has the advantages of wide spatial coverage and objective and repeatable data with high temporal resolution that can be analyzed by computer conveniently. Generally, the methods can be divided into four types, namely, the multi-/hyper-remote sensing, the thermal remote sensing, the microwave remote sensing, and three dimensional reconstructionbased on the categories of the sensors used in pavement monitoring. The multi-/hyper- remote sensing primarily utilizes the reflective spectral information of the pavement, which contains three specific methods, namely, image processing method, the method based on the variation of pavement brightness, and spectral modeling. The thermal remote sensing is based on the radiation characteristics of pavement, which contains two methods based on the variation of pavement temperature and emissivity information. A microwave device called Ground Penetrating Radar (GPR) has been widely used to detect the pavement defects by road departments. The three dimensional information of the road surface can be acquired using photogrammetry and LiDAR systems to measure the elevation information of the deteriorated pavement. An experiment using the Multiple Endmember Spectral Mixing Analysis (MESMA) and WorldView-2 satellite image was conducted to evaluate the potential of remote sensing in aging pavement monitoring. Results show that all methods are greatly potential in pavement condition monitoring. However, these methods still have some limitations in terms of complexity, accuracy, and robustness. High resolution remote sensing technologies, such as sub-meter satellite data and low-altitude UAV systems, can achieve large-range and rapid detection of the road pavement conditions in question. The results of the case study in Liangxiang area of Beijing show that the road pavement aging conditions could be detected appropriately and mapped with higher accordance with the practical pavement conditions. Therefore, some ground remote sensing methods have been applied successfully in pavement condition detection, such as the Pavement Management Systems (PMS), GPR, and so on. However, spaceborne and airborne remote sensing methods still encounter problems (poor robustness and low accuracy etc.)on pavement condition detection. For instance, the applicability of pavement spectral analysis and airborne or satellite data in pavement condition detection applications still require additional future works. Finally, a case study using satellite multispectral data to monitor asphalt pavement aging conditions is presented to demonstrate the usefulness of remote sensing technology in road pavement monitoring and assessment.

مفهوم

References

  1. 1.
    Ahmed M, Haas C T and Haas R. 2011. Toward low-cost 3D automatic pavement distress surveying: the close range photogrammetry approach. Canadian Journal of Civil Engineering, 38(12): 1301–1313
  2. 2.
    Branco L H C and Segantine P C L. 2015. MaNIAC-UAV-a methodology for automatic pavement defects detection using images obtained by Unmanned Aerial Vehicles. Journal of Physics: Conference Series, 633(1): 012122
  3. 3.
    Chambon S and Moliard J M. 2011. Automatic road pavement assessment with image processing: review and comparison. International Journal of Geophysics, 2011: 989354
  4. 4.
    Cline G D, Shahin M Y and Burkhalter J A. 2003. Automated data collection for pavement condition index survey//Proceedings of 20 03 Annual Meeting of Transportation Research Board. Washington: TRB: 1–16
  5. 5.
    Emery W, Yerasi A, Longbotham N and Pacifici F. 2014. Assessing paved road surface condition with high-resolution satellite imagery//Proceedings of 2014 IEEE International Geoscience and Remote Sensing Symposium. Québec: IEEE: 1–16
  6. 6.
    Feng W, Wu Y D and Qiang Z. 2009. UAV borne real-time road mapping system//Proceedings of 2009Joint Urban Remote Sensing Event. Shanghai: IEEE: 1–7
  7. 7.
    Gan L, Gan L C, Tian M and Zhao M T. 2008. Step-sampling system of high resolution ground penetrating radar. Chinese Journal of Radio Science, 23(3): 555–559
  8. 8.
    Gomez R B. 2002. Hyperspectral imaging: a useful technology for transportation analysis. Optical Engineering, 41(9): 2137–2143
  9. 9.
    Herold M and Roberts D. 2005. Spectral characteristics of asphalt road aging and deterioration: implications for remote-sensing applications. Applied Optics, 44(20): 4327–4334
  10. 10.
    Herold M, Roberts D A, Gardner M E and Dennison P E. 2004. Spectrometry for urban area remote sensing—development and analysis of a spectral library from 350 to 2400 nm. Remote Sensing of Environment, 91(3/4): 304–319
  11. 11.
    Herold M, Roberts D, Noronha V and Smadi O. 2008. Imaging spectrometry and asphalt road surveys. Transportation Research Part C: Emerging Technologies, 16(2): 153–166
  12. 12.
    Huang Y X and Xu B G. 2006. Automatic inspection of pavement cracking distress. Journal of Electronic Imaging, 15(1): 013017
  13. 13.
    Jemison H B, Burr B L, Davison R R, Bullin J A and Glover C J. 1992. Application and use of the ATR, FT-IR method to asphalt aging studies. Fuel Science and Technology International, 10(4/6): 795–808
  14. 14.
    Jin H. 2011. An Integrated Approach for Precise Road Reconstruction from Aerial Imagery and LiDAR Data. Brisbane, Australia: Queensland University of Technology
  15. 15.
    Jitprasithsiri S. 1997. Development of A New Digital Pavement Image Processing Algorithm for Unified Crack Index Computation. Utah, America: The University of Utah
  16. 16.
    Jol H M. 2008. Ground Penetrating Radar Theory and Applications. Oxford, UK: Elsevier [DOI: 10.1016/B978-0-444-53348-7.00016-8]
  17. 17.
    Kertész I, Lovas T and Barsi A. 2008. Photogrammetric pavement detection system//2008 XXXVII ISPRS Congress: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Beijing: ISPRS: 897–902
  18. 18.
    Kotthaus S, Smith T E L, Wooster M J and Grimmond C S B. 2014. Derivation of an urban materials spectral library through emittance and reflectance spectroscopy. ISPRS Journal of Photogrammetry and Remote Sensing, 94: 194–212
  19. 19.
    Liu S J. 2012. Research on the Testing Index on the Pavement Health Pavement Structure of Asphalt Pavement Structure. Xi’an: Chang’an University
  20. 20.
    Loizos A and Plati C. 2007. Accuracy of pavement thicknesses estimation using different ground penetrating radar analysis approaches. NDT andE International, 40(2): 147–157
  21. 21.
    Manzo C, Mei A, Salvatori R, Bassani C and Allegrini A. 2014. Spectral modelling used to identify the aggregates index of asphalted surfaces and sensitivity analysis. Construction and Building Materials, 61: 147–155
  22. 22.
    Mei A, Fiore N, Salvatori R, D’Andrea A and Fontana M. 2012. Spectroradiometric laboratory measures on asphalt concrete: preliminary results. Procedia-Social and Behavioral Sciences, 53: 514–523
  23. 23.
    Mei A, Salvatori R, Fiore N, Allegrini A and D’Andrea A. 2014. Integration of field and laboratory spectral data with multi-resolution remote sensed imagery for asphalt surface differentiation. Remote Sensing, 6(4): 2765–2781
  24. 24.
    Mettas C, Themistocleous K, Neocleous K, Christofe A, Pilakoutas K and Hadjimitsis D. 2015. Monitoring asphalt pavement damages using remote sensing techniques//Proceedings of SPIE 9535Third International Conference on Remote Sensing and Geoinformation of the Environment. Paphos, Cyprus: SPIE: 95350S [DOI: 10.1117/12.2195702]
  25. 25.
    Ministry of Transport of the People’s Republic of China. 2001. National Standard for Asphalt Pavement Maintenance. Beijing: Research Institute of Highway Ministry of Transport: 1–122.
  26. 26.
    Ministry of Transport of the People’s Republic of China. 2007. JTG H20-2007Highway Performance Assessment Standard. Beijing: Research Institute of Highway Ministry of Transport: 1–33
  27. 27.
    Mohammadi M. 2012. Road classification and condition determination using hyperspectral imagery//2012 XXII ISPRS Congress: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.Melbourne: ISPRS: 141–146
  28. 28.
    Mokhtari S. 2015. Analytical Study of Computer Vision-based Pavement Crack Quantification Using Machine Learning Techniques. Florida, America: University of Central Florida Orlando
  29. 29.
    Mokhtari S, Wu L L and Yun H B. 2016. Comparison of supervised classification techniques for vision-based pavement crack detection//Transportation Research Board 95th Annual Meeting. Washington: 1–17
  30. 30.
    Moropoulou A, Avdelidis N, Koui M and Kakaras K. 2001. An application of thermography for detection of delaminations in airport pavements. NDT and E International, 34(5): 329–335
  31. 31.
    Noronha V, Herold M, Roberts D and Gardner M. 2002. Spectrometry and hyperspectral remote sensing for road centerline extraction and evaluation of pavement condition//National Consortium on Remote Sensing in Transportation—Infrastructure Management. California, America: National Consortium on Remote Sensing in Transportation: 1–12
  32. 32.
    Pan Y F, Zhang X F, Jin X, Yu H F, Rao J F, Tian S H, Luo L and Li C. 2016. Road pavement condition mapping and assessment using remote sensing data based on MESMA. IOP Conference Series: Earth and Environmental Science, 34(1): 012023
  33. 33.
    Pan Y F, Zhang X F, Tian J, Jin X, Luo L and Yang K. 2017. Mapping asphalt pavement aging and condition using multiple endmember spectral mixture analysis in Beijing, China. Journal of Applied Remote Sensing, 11(1): 016003
  34. 34.
    Pascucci S, Bassani C, Palombo A, Poscolieri M and Cavalli R. 2008. Road asphalt pavements analyzed by airborne thermal remote sensing: preliminary results of the Venice Highway. Sensors, 8(2): 1278–1296
  35. 35.
    Paska E P. 2009. State-of-the-Art Remote Sensing Geospatial Technologies in Support of Transportation Monitoring and Management. Columbus, OH, America: The Ohio State University
  36. 36.
    Peng B, Jiang Y S, Han S F, Luo N X. 2014. A review of automatic pavement crack image recognition algorithms. Journal of Highway and Transportation Research and Development, 31(7): 19–25
  37. 37.
    Puente I, Solla M, González-Jorge H and Arias P. 2013. Validation of mobile LiDAR surveying for measuring pavement layer thicknesses and volumes. NDT and E International, 60: 70–76
  38. 38.
    Roberts D A, Gardner M, Church R, Ustin S, Scheer G and Green R O. 1998. Mapping chaparral in the Santa Monica Mountains using multiple endmember spectral mixture models. Remote Sensing of Environment, 65(3): 267–279
  39. 39.
    Saar T and Talvik O. 2010. Automatic asphalt pavement crack detection and classification using neural networks//Proceedings of the 12th Biennial Baltic Electronics Conference. Tallinn, Estonia: IEEE: 345–348
  40. 40.
    Schnebele E, Tanyu B, Cervone G and Waters N. 2015. Review of remote sensing methodologies for pavement management and assessment. European Transport Research Review, 7: 1–19
  41. 41.
    Themistocleous K, Neocleous K, Pilakoutas K and Hadjimitsis D G. 2014. Damage assessment using advanced non-intrusive inspection methods: integration of space, UAV, GPR, and field spectroscopy//Proceedings of the SPIE 9229Second International Conference on Remote Sensing and Geoinformation of the Environment. Paphos, Cyprus: SPIE: 92291O [DOI: 10.1117/12.2069507]
  42. 42.
    Tiong P L Y, Mustaffar M and Hainin M R. 2012. Road surface assessment of pothole severity by close range digital photogrammetry method. World Applied Sciences Journal, 19(6): 867–873
  43. 43.
    Tong Q X, Zhang B, Zheng L F. 2006. Hyperspectral Remote Sensing: Principle, Method and Application. Beijing: Higher Education Press: 19–27
  44. 44.
    Uddin W. 2011. Remote sensing laser and imagery data for inventory and condition assessment of road and airport infrastructure and GIS visualization. International Journal of Roads and Airports, 1(1): 53–67
  45. 45.
    Uddin W and Al-Turk E. 2001. Airborne LIDAR digital terrain mapping for transportation infrastructure asset management//Proceedings of the Fifth International Conference on Managing Pavements. WashingtonSeattle, America: University of Washington: 11–14
  46. 46.
    Zhang C S and Elaksher A. 2012. An unmanned aerial vehicle-based imaging system for 3D measurement of unpaved road surface distresses. Computer-Aided Civil and Infrastructure Engineering, 27(2): 118–129
  47. 47.
    Zhang J Q, Pan L and Wang S G. 2003. Photogrammetry. Wuhan: Wuhan University Press: 30–56
  48. 48.
    Zhang S and Bogus S M. 2014. Use of low-cost remote sensing for infrastructure management//Construction Research Congress 2014. Reston, VA, USA: ASCE: 1299–1308
  49. 49.
    Zhao Y S. 2003. Principle and Methods of Remote Sensing. Beijing: Science Press: 104–129

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