Developments in the field of artificial intelligence have made great strides in the field of automatic semantic segmentation, both in the 2D (image) and 3D spaces. Within the context of 3D recording technology it has also seen application in several areas, most notably in creating semantically rich point clouds which is usually performed manually. In this paper, we propose the introduction of deep learning-based semantic image segmentation into the photogrammetric 3D reconstruction and classification workflow. The main objective is to be able to introduce semantic classification at the beginning of the classical photogrammetric workflow in order to automatically create classified dense point clouds by the end of the said workflow. In this regard, automatic image masking depending on pre-determined classes were performed using a previously trained neural network. The image masks were then employed during dense image matching in order to constraint the process into the respective classes, thus automatically creating semantically classified point clouds as the final output. Results show that the developed method is promising, with automation of the whole process feasible from input (images) to output (labelled point clouds). Quantitative assessment gave good results for specific classes e.g., building facades and windows, with IoU scores of 0.79 and 0.77 respectively.

This paper reports the knowledge process and the analyses performed to assess the seismic behavior of a heritage masonry building. The case study is a three-story masonry building that was the house of the Renaissance architect and painter Giorgio Vasari (the Vasari’s House museum). An interdisciplinary approach was adopted, following the Italian “Guidelines for the assessment and mitigation of the seismic risk of the cultural heritage”. This document proposes a methodology of investigation and analysis based on three evaluation levels (EL1, analysis at territorial level; EL2, local analysis and EL3, global analysis), according to an increasing level of knowledge on the building. A comprehensive knowledge process, composed by a 3D survey by Terrestrial Laser Scanning (TLS) and experimental in situ tests, allowed us to identify the basic structural geometry and to assess the value of mechanical parameters subsequently needed to perform a reliable structural assessment. The museum represents a typology of masonry building extremely diffused in the Italian territory, and the assessment of its seismic behavior was performed by investigating its global behavior through the EL1 and the EL3 analyses

Abstract. The interest in high-resolution semantic 3D models of historical buildings continuously increased during the last decade, thanks to their utility in protection, conservation and restoration of cultural heritage sites. The current generation of surveying tools allows the quick collection of large and detailed amount of data: such data ensure accurate spatial representations of the buildings, but their employment in the creation of informative semantic 3D models is still a challenging task, and it currently still requires manual time-consuming intervention by expert operators. Hence, increasing the level of automation, for instance developing an automatic semantic segmentation procedure enabling machine scene understanding and comprehension, can represent a dramatic improvement in the overall processing procedure. In accordance with this observation, this paper aims at presenting a new workflow for the automatic semantic segmentation of 3D point clouds based on a multi-view approach. Two steps compose this workflow: first, neural network-based semantic segmentation is performed on building images. Then, image labelling is back-projected, through the use of masked images, on the 3D space by exploiting photogrammetry and dense image matching principles. The obtained results are quite promising, with a good performance in the image segmentation, and a remarkable potential in the 3D reconstruction procedure.