The study presents an innovative approach to classify geomaterials using supervised classification methods from orthophotos derived from UAV (Unmanned Aerial Vehicle) and photogrammetric processing. The case study examined is the Ponte Rotto, dating back to 20 BC, which in antiquity allowed the Appian Way to cross the Calore River – between the provinces of Avellino and Beneven-to – to continue towards the port of Brindisi. In previous studies, experts on geomaterial diagnosis estimated – from aerophotogrammetric orthophotos generated for both bridge elevations – the geo-materials and quantities used for the construction of the monument and an overview of the state of conservation of the monument studied. Orthophotos of facades were imported into CAD software and used as the basis for – according to a manual process – the mapping of the materials. The work presents the results according to automatic Machine Learning clustering from the same orthophotos to identify geomaterials.
Image–Based Modelling Restitution: Pipeline for Accuracy Optimisation
The paper presents an innovative approach to support survey methods by applying AI algorithms to improve the accuracy of point clouds generated from UAV imagery. The work analyses different photogrammetric accuracy parameters in a first step, such as reprojection error and the intersection angle between homologous rays, verifying that a single parameter is enough to evaluate the accuracy of the photogrammetric restitution. Therefore, some of the calculated parameters were analysed through a Self–Organizing Map (SOM) to reach a compromise between the value of the variables analysed and the noise reduction associated with the 3D model definition. In the case study, it has been observed that the parameter that most influences the noise in the photogrammetric point clouds is the intersection angle.