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Abstract. The general purpose of the paper is the study of surveying and data processing methodologies that are efficient to obtain more detailed metric data on road infrastructures than can be derived from classical surveying techniques. The inspection and monitoring of the condition of an infrastructure are two essential steps to increase the users' safety and to properly manage the available resources and are a preparatory step to the subsequent steps of deciding on the interventions to be put in place. Analysis of the state of degradation, if conducted with traditional methodologies, can be risky and sometimes inefficient. The Mobile Laser Scanner (MLS) technique, based on LiDAR (Light Detection and Ranging) technology, is also widely used today as an alternative to traditional techniques since it allows obtaining dense and accurate point clouds of the road surface. The purpose of our work is to provide a workflow for the processing of MLS data aimed at producing some useful indicators to describe the functional and structural characteristics of the pavement, with the goal of optimizing the decision-making processes of the Managing Authority. Specifically, the data flow was studied, and several processing algorithms were implemented to identify and quantify surface defects and road roughness. The result of the entire process is the creation of an Atlas in QGIS to create graphical tables related to each individual cross profile and that can be used to identify all those sections that need emergency actions and therefore characterized by a high priority of intervention.
Benedetto et al. (Thu,) studied this question.