Use of UAV-based photogrammetry products for semi-automatic detection and classification of asphalt road damage in landslide-affected areas

N. Nappo*, O. Mavrouli, F. Nex, C. van Westen, Roberto Gambillara, Alessandro Maria Michetti

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)
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Transportation networks are severely affected by natural hazards, including landslides. The prioritization of maintenance works is required to preserve the efficiency and functionality of road infrastructure. To overcome the subjectivity of traditional visual inspections for road pavement condition assessment, advanced (semi-)automatic approaches have been emerging. Still, the quantitative and objective description of damage typology and extent, and its severity classification remain the major issues for the assessment of landslide impacts on transportation routes. The objective of this work is to provide a ready-to-use tool for semi-automatic damage assessment of asphalt-paved roads in landslide-affected areas to support risk analysis and planning of mitigation measures. The use of 3D models and 2D images as reconstructed from UAV-based photogrammetry is investigated to detect longitudinal and transverse cracks on the road pavement and assess their severity in landslide areas, as a rapid, systematic, objective and less laborious alternative to traditional field surveys. A semi-automatic procedure is proposed to i) select asphalt-paved roads exposed to landslides, ii) rapidly map distresses on selected road sections, iii) quantitatively detect and describe longitudinal and transverse cracks, and iv) classify their severity according to the International Roughness Index (IRI). The procedure is applied to the Province of Como (northern Italy), where three test sites are selected for detailed analyses. The results indicate that the proposed procedure is useful for road management purposes at different levels of details by providing four outputs: i) a road damage hotspot map to detect deformations, ii) a multi-criteria binary classifier to detect pavement damage, iii) an IRI-based criterion to rate the pavement quality, and iv) a road damage severity map.

Original languageEnglish
Article number106363
Number of pages29
JournalEngineering geology
Early online date3 Sep 2021
Publication statusPublished - 5 Dec 2021


  • Landslide
  • Pavement distress
  • Photogrammetry
  • Road damage
  • Road monitoring
  • UAV
  • UT-Hybrid-D


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