• Helik Susilo Politeknik Negeri Malang
  • Martince Novianti Bani Jurusan Teknik Sipil, Politeknik Negeri Malang
  • Muhammad Tri Aditya Jurusan Teknik Sipil, Politeknik Negeri Malang
  • Eri Cahyani Jurusan Teknik Sipil, Politeknik Negeri Malang
  • Achendri M. Kurniawan Jurusan Teknik Sipil, Politeknik Negeri Malang
Abstract views: 20 , PDF downloads: 20
Keywords: Inspection, Road Damage, UAV-Photogrammetry


Survey of road damage data can be conducted by the direct survey method or manual inspection, but that mothod is quite long and requires a lot of employers, so it is not effective and efficient. This research focuses on the inspection of road damage using drone technology by taking aerial photos using the UAV-Photogrammetry method. Aerial photography was carried out along the roads in the study area. The partial aerial photos are processed using image processing software to become the orthophoto map and digital elevation model (DEM). Road damage data identification was carried out by measuring the dimensions (length, width, and depth) from the orthophoto map and DEM by the visual interpretation method. The research results show that the types of road damage identified from the orthophoto map and DEM in the study area are potholes, block cracks, continuous cracks, and patches. The accuracy of the dimensions of road damage produced from the orthophoto map and DEM compared to the dimensions of road damage measured directly has different values for horizontal of 0.001 - 0.088 m and vertical of 0.010 - 0.019 m, RMSE values range from 0.005 to 0.058. The results of the t-test statistical test show that there is no significant difference between measurements of road damage dimensions from the orthophoto map and DEM and the results of direct measurements.


H. Eisenbeiß, “UAV Photogrammetry,” 2009.

D. A. Roberts and M. Herold, “Mapping asphalt road conditions with hyperspectral remote sensing,” 2005. [Online]. Available:,

P. L. Y. Tiong, M. Mustaffar, and M. B. Rosli, “Road surface assessment of pothole severity by close range digital photogrammetry method PTSF on Two-lane Highways View project Sustainable Performance of Formwork System in Malaysia View project,” 2012, doi: 10.5829/idosi.wasj.2012.19.06.3353.

G. Leonardi, V. Barrile, R. Palamara, F. Suraci, and G. Candela, “3D mapping of pavement distresses using an Unmanned Aerial Vehicle (UAV) system,” in Smart Innovation, Systems and Technologies, Springer Science and Business Media Deutschland GmbH, 2019, pp. 164–171. doi: 10.1007/978-3-319-92102-0_18.

A. Ragnoli, M. R. De Blasiis, and A. Di Benedetto, “Pavement distress detection methods: A review,” Infrastructures, vol. 3, no. 4. MDPI Multidisciplinary Digital Publishing Institute, Dec. 19, 2018. doi: 10.3390/infrastructures3040058.

C. Koch, K. Georgieva, V. Kasireddy, B. Akinci, and P. Fieguth, “A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure,” Advanced Engineering Informatics, vol. 29, no. 2, pp. 196–210, Apr. 2015, doi: 10.1016/j.aei.2015.01.008.

S. C. Radopoulou, I. Brilakis, K. Doycheva, and C. Koch, “A Framework for Automated Pavement Condition Monitoring,” in Construction Research Congress 2016: Old and New Construction Technologies Converge in Historic San Juan - Proceedings of the 2016 Construction Research Congress, CRC 2016, American Society of Civil Engineers (ASCE), 2016, pp. 770–779. doi: 10.1061/9780784479827.078.

E. Schnebele, B. F. Tanyu, G. Cervone, and N. Waters, “Review of remote sensing methodologies for pavement management and assessment,” European Transport Research Review, vol. 7, no. 2, Jun. 2015, doi: 10.1007/s12544-015-0156-6.

I. Mandaya and Harintaka, “Pemanfaatan Teknologi UAV (Unmanned Aerial Vehicle) Untuk Identifikasi Dan Klasifikasi - Jenis Kerusakan Jalan,” Rekayasa Sipil, vol. 14, no. 3, 2020, Accessed: Sep. 23, 2023. [Online]. Available:

H. Svatonova, “Analysis of visual interpretation of satellite data,” in International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, International Society for Photogrammetry and Remote Sensing, 2016, pp. 675–681. doi: 10.5194/isprsarchives-XLI-B2-675-2016.

M. Zeybek, S. Bicici, T. Uzaktan Algılama ve CBS Dergisi, and S. Biçici, “Road Distress Measurements Using UAV Application of LiDAR Systems in Forest Ecosystems View project Road Surface Monitoring with UAV View project Turkish Journal of Remote Sensing and GIS Road Distress Measurements Using UAV İHA Kullanarak Yol Bozukluk Ölçmeleri,” 2020.

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How to Cite
Susilo, H., Bani, M. N., Aditya, M. T., Cahyani, E., & Kurniawan, A. M. (2024). ANALYSIS OF ROAD DAMAGE DETECTION USING ORTHOPHOTO MAP FROM UNMANNED AERIAL VEHICLE (UAV-PHOTOGRAMMETRY). Jurnal Qua Teknika, 14(1), 53-65.