From HBIM to Point Cloud Applications: Heritage Digital Data Management in Parametric and Informative Environments

The application of information systems to the documentation and representation of historical–architectural heritage is currently the focus of research, experimentations, and innovations increasingly geared towards awareness, management, and conservation processes. This involves addressing unresolved challenges arising from the effort of translating the inherent complexity of heritage into knowledge that can be applied to monitoring, conservation, informative, cross-relational, and interdisciplinary actions. Processes involving data classification, segmentation, and semantic association for high-level knowledge clustering, exploiting Artificial Intelligence algorithms, are emerging as a potential—albeit ambivalent—aid in the management of digital information sources. The paper explores some State of the Art procedures in the field and ongoing applied research with a particular focus on the concept of adaptive data management, leveraging parametric modeling and applications within 3D point cloud data for the recognition of surface features and diagnostic purposes. The reconciliation of information between the point cloud segmented through Artificial Intelligence algorithms and the HBIM model starts from heritage building laser scanner and photomodeling datasets, analyzing materials and state of conservation features. The experimentation is focused on a reverse approach—the so-called “HBIM-to-Cloud”—with the aim of generating an enriched information cloud defined by the surfaces of the parametric model.