Cultural Heritage has been significantly impacted by advancements in the Information and Communications Technology domains, which have inspired a strong multidisciplinary interest and enabled the development of innovative strategies for the preservation, management, and enhancement of the heritage itself. Notably, the digitisation process, which entails the acquisition of 3D data obtained through cutting-edge LiDAR and photogrammetric scanning techniques, is set up as an advantageous tool for producing an accurate representation of the historical buildings. In addition, point clouds and reliable HBIM models have caught the minds of the architectural community, and are now receiving huge backing from Artificial Intelligence. Such support is provided by procedures that link semantic features to structural and decorative elements. In this scenario, the following research is presented: the aim is to test an automated iterative process within a scan-to-BIM methodology, starting from automatic point cloud segmentation operations with open-source, model-fitting algorithms. This method will prove to be a solid support for the final phase of the 3D parametric/adaptive reconstruction that’s also compatible with BIM Authoring. The study focuses on various masonry vaulted systems. These types of structures are first examined using ideal models, which were perfectly discretised and set up by the user, and then employed as a starting point for validating the parameters of the RANSAC algorithm on point clouds acquired by laser scanners. These latter ones nevertheless have irregular geometries, making comprehension, analysis, and management far more challenging.

In the current landscape dominated by Artificial Intelligence, the integration of Machine Learning and Deep Learning within the realm of Cultural Heritage, particularly within architectural contexts, is paramount for the efficient processing and interpretation of point clouds. These advanced methods facilitate automated segmentation and classification, significantly improving both the clarity and practical use of data acquired from laser scanning and photogrammetry. The present study investigates the Palacio de Sástago—a prominent Renaissance palace in Zaragoza, Spain—and introduces a cutting-edge modus operandi for the automated recognition of architectural elements within the palace’s inner courtyard. Employing the well-established Random Forest algorithm, implemented in a Python environment, the framework begins with a comprehensive evaluation of the geometric features identified in the LiDAR point cloud. This process employs the Mean Decrease in Impurity metric to evaluate the relevance of each variable. To boost the accuracy and efficiency of the final classifications, the features are refined post-assessment, enhancing both the training phase and the algorithm’s later evaluation. The research’s findings demonstrate significant potential, supporting advancements in CAD systems and HBIM that will enable more precise, automated modelling of architectural elements, thereby enhancing the accuracy of digital reconstructions and improving conservation planning for heritage sites.

The last decade has seen the development of a growing tendency to use the most modern technologies, in the field of Cultural Heritage, with the aim of digitizing and facilitating protection and conservation activities. Much research has focused on the development of innovative methods such as photogrammetry or Terrestrial Laser Scanners, in terms of reliability, precision, time and costs. In this research, however, the use of the smartphone was investigated by comparing the point clouds obtained via videogrammetry from smartphones, with those generated by different digital survey techniques, such as Terrestrial Laser Scanners and photogrammetry via SLR camera. Specifically, a smartphone was used and the comparison between point clouds was conducted based on four criteria: point clouds fitting, density evaluation, profiling and texture quality with the aim, therefore, of verifying the geometric reliability of the data and the quality of polygonal lines and the mesh/texture derived. The case study selected was the sepulchral monument of the Pascopepe Lambertini family (14th century), located in the Crypt of Santa Maria della Scala, the Cathedral of Trani (South of Italy). Finally, the research demonstrates how this methodology, in the documentation of heritage, allows for even greater portability and accessibility compared to other methodologies, maintaining widely acceptable standards of accuracy and therefore going to constitute a valid alternative in the documentation of historical heritage.