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.

交通网络受到包括山体滑坡在内的自然灾害的严重影响。需要对维护工作进行优先排序，以保持道路基础设施的效率和功能。为了克服道路路面状况评估的传统目视检查的主观性，先进的（半）自动方法已经出现。尽管如此，对破坏类型和范围的定量和客观描述及其严重程度分类仍然是评估滑坡对运输路线影响的主要问题。这项工作的目的是提供一种现成的工具，用于对受滑坡影响地区的沥青路面进行半自动损坏评估，以支持风险分析和缓解措施的规划。研究使用从基于无人机的摄影测量重建的 3D 模型和 2D 图像来检测道路路面上的纵向和横向裂缝并评估它们在滑坡区域的严重程度，作为传统现场调查的快速、系统、客观和省力的替代方案. 建议采用半自动程序：i) 选择暴露于滑坡的沥青路面，ii) 快速绘制所选路段上的故障图，iii) 定量检测和描述纵向和横向裂缝，以及 iv) 根据国际标准对其严重程度进行分类粗糙度指数（IRI）。该程序适用于科莫省（意大利北部），在那里选择了三个测试地点进行详细分析。