Existing reinforced concrete (RC) bridges that were designed in the decades between 1950 and 1990 exhibit inadequate structural safety with reference to both traffic loads and hazard conditions. Competent authorities are planning extensive inspections to collect data about these structures and to address retrofit interventions. In this context, Remotely Piloted Aircraft Systems (RPASs) represent a prospect to facilitate in-situ inspections, reducing time, cost and risk for the operators. A practice-oriented methodology to perform RPAS-based surveys is described. After that, a workflow to perform an in-situ RPAS inspection oriented to a photogrammetric data extraction is discussed. With the aim to connect the advantages of the RPAS technologies to the seismic risk assessment of bridges, a simplified mechanic-based procedure is described, oriented to map the structural risk in road networks and support prioritization strategies. A six-span RC bridge of the Basilicata road network, representing a typical Italian bridge typology is selected to practically describe the operating steps of the RPAS inspection and of the simplified seismic risk assessment approach.

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A wide range of industrial applications benefits from the accessibility of image-based techniques for three-dimensional modelling of different multi-scale objects. In the last decade, along with the technological progress mainly achieved with the use of Unmanned Aerial Vehicles (UAVs), there has been an exponential growth of software platforms enabled to return photogrammetric products. On the other hand, the different levels of final product accuracy resulting from the adoption of different processing approaches in various softwares have not yet been fully understood. To date, there is no validation analysis in literature focusing on the comparability of such products, not even in relation to the use of workflows commonly allowed inside various software platforms. The lack of detailed information about the algorithms implemented in the licensed platforms makes the whole interpretation even more complex. This work therefore aims to provide a comparative evaluation of three photogrammetric softwares commonly used in the industrial field, in order to obtain coherent, if not exactly congruent results. After structuring the overall processing workflow, the processing pipelines were accurately parameterized to make them comparable in both licensed and open-source softwares. For the best interpretation of the results derived from the generation of point clouds processed by the same image dataset, the obtainable values of root-mean-square error (RMSE) were analyzed, georeferencing models as the number of GCPs varied. The tests carried out aimed at investigating the elements shared by the platforms tested, with the purpose of supporting future studies to define a unique index for the accuracy of final products.

Remotely piloted aerial systems (RPAS) have been recognized as an effective low-cost tool to acquire photogrammetric data of low accessible areas reducing collection and processing time. Data processing techniques like structure from motion (SfM) and multiview stereo (MVS) techniques, can nowadays provide detailed 3D models with an accuracy comparable to the one generated by other conventional approaches. Accuracy of RPAS-based measures is strongly dependent on the type of adopted sensors. Nevertheless, up to now, no investigation was done about relationships between camera calibration parameters and final accuracy of measures. In this work, authors tried to fill this gap by exploring those dependencies with the aim of proposing a prediction function able to quantify the potential final error in respect of camera parameters. Predictive functions were estimated by combining multivariate and linear statistical techniques. Four photogrammetric RPAS acquisitions were considered, supported by ground surveys, to calibrate the predictive model while a further acquisition was used to test and validate it. Results are preliminary, but promising. The calibrated predictive functions relating camera internal orientation (I.O.) parameters with final accuracy of measures (root mean squared error) showed high reliability and accuracy.

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Frequently exposed to natural agents such as waves, wind, tides, storm activity, seasonal changes and anthropogenic agents, coastal areas are tangibly high energy environments and therefore subject to considerable dynamics. In order to mitigate and reduce the impacts on these areas, different types of coastal protection systems can be implemented. Rockwalls and breakwaters are the most ordinary structures and even if used precisely for coastal protection, these flexible structures can in turn be damaged or ineffective over time. Therefore, like the monitoring of coastal areas in terms of execution frequency and accuracy, the measurement of changes over time of these structures, in particular after significant events, can allow to carry out an economic maintenance service before a serious occurrence and costly damage. However, given the rapid evolution of the preservation state of coastal areas and protection structures, it is therefore essential to plan an equally frequent, practical and accurate structural-coastal monitoring. On the other hand, their accessibility can sometimes be dangerous or uncomfortable such as to compromise operations in the field. In this work, the application of two close-range detection techniques competitor, i.e. from the Terrestrial Laser Scanner and from Remote Piloted Aircraft Systems, aimed at the generation of three-dimensional reconstructions of a protection structure, was analyzed. By performing a cloud-to-cloud comparison, interesting considerations have been obtained on the precision that can be achieved and on the technical limits deriving from the two methodologies. Considering the economy and practicality of use, if used correctly, a Remote Piloted Aircraft Systems supported by a suitable geo-referencing and an optimized data processing, can produce accurate and coherent 3D reconstructions as those derivable from the Terrestrial Laser Scanner. Finally, the results obtained by merging the point clouds generated from the two different techniques were evaluated in order to identify any advantages in the structural maintenance of the systems.

The consolidation of unmanned aerial vehicle (UAV) photogrammetric techniques for campaigns with high and medium observation scales has triggered the development of new application areas. Most of these vehicles are equipped with common visible-band sensors capable of mapping areas of interest at various spatial resolutions. It is often necessary to identify vegetated areas for masking purposes during the postprocessing phase, excluding them for the digital elevation models (DEMs) generation or change detection purposes. However, vegetation can be extracted using sensors capable of capturing the near-infrared part of the spectrum, which cannot be recorded by visible (RGB) cameras. In this study, after reviewing different visible-band vegetation indices in various environments using different UAV technology, the influence of the spatial resolution of orthomosaics generated by photogrammetric processes in the vegetation extraction was examined. The triangular greenness index (TGI) index provided a high level of separability between vegetation and nonvegetation areas for all case studies in any spatial resolution. The efficiency of the indices remained fundamentally linked to the context of the scenario under investigation, and the correlation between spatial resolution and index incisiveness was found to be more complex than might be trivially assumed.

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