Standard quantification and measurement of damages through features characterization of surface imperfections on 3D models: an application on Architectural Heritages

Abstract Reverse Engineering techniques lead to easily obtain, even in case of wide and complex objects, high-resolution 3D models, suitably adoptable in the field of surface analysis and characterization. This research aims to propose innovative quantification and measuring approaches to diagnose and monitor damages affecting artefacts of different nature, from manufacturing to architectural heritage, performing non-destructive analyses with advanced surface metrology instruments and the potential integrations of the existing sectorial standards. General condition assessment is proposed to recognize and classify characterized pathologies by meaningful features in the form of surface imperfections, through the analysis of acquired point clouds. The method is applied to decay phenomena of an architectural artefact

Decay detection in historic buildings through image-based deep learning

Nowadays, built heritage condition assessment is realized through on-site or photo-aided visual inspections, reporting pathologies manually on drawings, photographs, notes. The knowledge of the state of conservation goes through subjective and time or cost consuming procedures. This is even relevant for a historic building characterized by geometrical and morphological complexity and huge extension, or at risk of collapse. In this context, advancements in the field of Computer Vision and Artificial Intelligence provide an opportunity to address these criticalities. The proposed methodology is based on a Mask R-CNN model, for the detection of decay morphologies on built heritages, and, particularly on historic buildings. The experimentation has been carried out and validated on a highly heterogeneous dataset of images of historic buildings, representative of the regional Architectural Heritage, such as: castles, monasteries, noble buildings, rural buildings. The outcomes highlighted the significance of this remote, non-invasive inspection technique, in support of the technicians in the preliminary knowledge of the building state of conservation, and, most of all, in the decay mapping of some particular classes of alterations (moist area, biological colonization)

AUTOMATIC POINT CLOUD SEGMENTATION FOR THE DETECTION OF ALTERATIONS ON HISTORICAL BUILDINGS THROUGH AN UNSUPERVISED AND CLUSTERING-BASED MACHINE LEARNING APPROACH

Abstract. The article describes an innovative procedure for the three-dimensional analysis of decay morphologies of ancient buildings, through the application of machine learning methods for the automatic segmentation of point clouds. In the field of Cultural Heritage conservation, photogrammetric data can be exploited, for diagnostic and monitoring support, to recognize different typologies of alterations visible on the masonry surface, starting from colour information. Actually, certain stone and plaster surface pathologies (biological patina, biological colonization, chromatic alterations, spots,…) are typically characterized by chromatic variations. To this purpose, colour-based segmentation with hierarchical clustering has been implemented on colour data of point clouds, considered in the HSV colour-space. In addition, geometry-based segmentation of 3D reconstructions has been performed, in order to identify the main architectural elements (walls, vaults), and to associate them to the detected defects. The proposed workflow has been applied to some ancient buildings' environments, chosen because of their irregularity both in geometrical and colorimetric characteristics