Iradaf Mandaya, Harintaka Harintaka


Roads are means of transportation that play an important role in supporting people's lives such as economic, social, educational and cultural activities. In its service, the age of road is often not as planned because it has damage to the surface layer. Maintenance, repair and rehabilitation efforts are needed. However, before that steps are needed to identify each type and level of damage that occurs so that recommendations can be proposed for improvement. Road distresses identification is still done manually by surveyors who fill out forms and sketches of road distress. On the other hand, UAV (Unmaned Aerial Vehicle) which have many advantages, such as: save time, money, labor and able to produce a geographical database for transportation issue. In this research data acquisition use automatic mode along road corridor. Aerial photo data is processed and analyzed by using SfM (structure from Motion) software to produce DSM (Digital Surface Model) and orthophoto. DSM is used to determine the depth profile of road distress. While the orthophoto results are then used in the process of visual interpretation to identify road distress. This research using quadcopter platform using digital camera with flight altitude less than 20 metres to obtain precise DSM and orthophoto. The results obtained by several types of road distress can be identified by using UAVs in the form of alligator cracking, potholes, edge cracking, shoving and depression. This type of road distress classification was obtained based on visual interpretation obtained an accuracy rate of 96.36 %.


aerial photo; dsm; quadcopter; road distress; structure from motion; orthophoto; uav

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T.Fwa, The Handbook of Highway Engineering. Florida, 2006.

M. Uljarevic and S. Supic, “Comparative Analysis of Flexible and Rigid Pavement Design,” Contemp. Constr. Achiev. Civ. Eng., pp. 835–844, 2016.

M. Herold and D. Roberts, “Spectral characteristics of asphalt road aging and deterioration : implications for remote-sensing applications,” Appl. Opt., vol. 44, no. 20, 2005.

N. Bandara, “Current and Future Pavement Maintenance Prioritization Based on Rapid Visual Condition Evaluation,” J. Transp. Eng. March 2001, no. April, pp. 116–123, 2001.

E. Schnebele, “Review of remote sensing methodologies for pavement management and assessment,” Eur. Transp. Res. Rev., 2015.

W.Miller, J.S. & Bellinger, “Distress Identification Manual for the Long-Term Pavement Performance Program,” in U.S Department of Transportation Federal Highway Administration, no. May, U.S Department of Transportation Federal Highway Administration, 2014.

J. Everaerts, “The Use of Unmanned Aerial Vehicles (UAVs) for Remote Sensing and Mapping,” Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. Beijing, vol. XXXVII, pp. 1187–1192, 2008.

N. Arsyad, “Akurasi Citra data Foto Udara Persimpangan Lalu Lintas Kota Kendari,” Rekayasa Sipil, vol. 14, no. 1, pp. 51–59, 2020.

J. P. Dash, M. S. Watt, G. D. Pearse, M. Heaphy, and H. S. Dungey, “ISPRS Journal of Photogrammetry and Remote Sensing Assessing very high resolution UAV imagery for monitoring forest health during a simulated disease outbreak,” ISPRS J. Photogramm. Remote Sens., vol. 131, pp. 1–14, 2017.

S. Valkaniotis, G. Papathanassiou, and A. Ganas, “Mapping an earthquake-induced landslide based on UAV imagery ; case study of the 2015 Okeanos landslide , Lefkada , Greece,” Eng. Geol., vol. 245, no. August, pp. 141–152, 2018.

M. Arturo and A. Ndoma, “The Uses of Unmanned Aerial Vehicles - UAV’s- (or drones) in Social Logistic : Natural Disasters Responses and Humanitarian Relief Aid,” Procedia Comput. Sci., vol. 149, pp. 375–383, 2019.

et al Ragnoli, “Pavement Distress Detection Methods: A Review,” Infrastructures, vol. 3, no. 4, p. 58, 2018.

S. Radopoulou, “A Framework for Automated Pavement Condition Monitoring,” Constr. Res. Congr., no. Asce 2013, pp. 770–779, 2016.

S. C. Radopoulou and I. Brilakis, “Automation in Construction Patch detection for pavement assessment,” Autom. Constr., vol. 53, pp. 95–104, 2015.

T. Luhmann, Close Range Photogrammetry, vol. 30, no. 151. 2011.

M. J. Westoby, “‘Structure-from-Motion’ photogrammetry: A low-cost, effective tool for geoscience applications,” Geomorphology, vol. 179, pp. 300–314, 2012.

C. Zhang and T. Chen, “Efficient Feature Extraction for 2D/3D Objects In Mesh Representation,” Virtual Real., pp. 1–4, 2001.

J. Aber, I. Marzolff, and J. Ries, Small-Format Aerial Photography, First., vol. 25, no. 11. 2010.

M. Prajwal, “Optimal Number of Ground Control Points for a UAV based Corridor Mapping,” Int. J. Innov. Res. Sci. Eng. Technol., vol. 5, no. 9, pp. 28–32, 2016.

H. Svatonova, “Analysis of visual interpretation of satellite data,” Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. - ISPRS Arch., vol. 41, no. July, pp. 675–681, 2016.

R. G. Congalton, “Accuracy Assessment and Validation of Remotely Sensed and Other Spatial Information,” Int. J. Wildl. Fire, vol. 10, no. 3–4, pp. 321–328, 2001.


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