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The reconstruction of 3D geometries starting from reality-based data is challenging and time-consuming due to the difficulties involved in modeling existing structures and the complex nature of built heritage. This paper presents a methodological approach for the automated segmentation and classification of surveying outputs to improve the interpretation and building information modeling from laser scanning and photogrammetric data. The research focused on the surveying of reticular, space grid structures of the late 19th–20th–21st centuries, as part of our architectural heritage, which might require monitoring maintenance activities, and relied on artificial intelligence (machine learning and deep learning) for: (i) the classification of 3D architectural components at multiple levels of detail and (ii) automated masking in standard photogrammetric processing. Focusing on the case study of the grid structure in steel named La Vela in Bologna, the work raises many critical issues in space grid structures in terms of data accuracy, geometric and spatial complexity, semantic classification, and component recognition. [ABSTRACT FROM AUTHOR]

We propose a semi-automatic Scan-to-BIM reconstruction approach, making the most of Artificial Intelligence (AI) techniques, for the classification of digital architectural heritage data. Nowadays, Heritage- or Historic-Building Information Modeling (H-BIM) reconstruction from laser scanning or photogrammetric surveys is a manual, time-consuming, overly subjective process, but the emergence of AI techniques, applied to the realm of existing architectural heritage, is offering new ways to interpret, process and elaborate raw digital surveying data, as point clouds. The proposed methodological approach for higher-level automation in Scan-to-BIM reconstruction is threaded as follows: (i) semantic segmentation via Random Forest and import of annotated data in 3D modeling environment, broken down class by class; (ii) reconstruction of template geometries of classes of architectural elements; (iii) propagation of template reconstructed geometries to all elements belonging to a typological class. Visual Programming Languages (VPLs) and reference to architectural treatises are leveraged for the Scan-to-BIM reconstruction. The approach is tested on several significant heritage sites in the Tuscan territory, including charterhouses and museums. The results suggest the replicability of the approach to other case studies, built in different periods, with different construction techniques or under different states of conservation. [ABSTRACT FROM AUTHOR]

Today, digital tools represent a privileged means of describing and understanding of cultural heritage. However, existing hybrid methods for semantic annotation on digital models are mostly manual and require considerable human intervention. This may cause data dispersion or data alteration issues, preventing the digital collection of multidisciplinary heritage information in collaborative valorization and conservation projects. Tackling this problem, this work presents three different methods for more automated 2D/3D data annotation, making the most of Machine Learning or Deep Learning and of the collaborative, web-based annotation platform Aïoli. The proposed approaches are designed to assist heritage experts in the hybrid annotation of architectural objects, e.g., to identify building components, degradation patterns, mapping of alterations, materials etc. and to share and access 2D/3D information via the web. The work aims at providing public and private stakeholders in charge of restoration and conservation activities with a set of more automated annotation tools, allowing to: i) preserve data in the transition to different representation types; ii) propagate analytically relevant information via digital models and/or images; iii) share heritage-related data on the web with an open source strategy. The validated workflows sketch the outlines of a dedicated and user-friendly toolbar for semi-automatic annotation, directly integrated into the Aïoli user interface, where automated annotation tools are readily available to the community of heritage professionals to properly support documentation and restoration activities. [ABSTRACT FROM AUTHOR]