H-BIM and Artificial Intelligence: Classification of Architectural Heritage for Semi-Automatic Scan-to-BIM Reconstruction
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- handle: 10670/1.ksf1w9
- hal: hal-04009294
- doi: 10.3390/s23052497
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info:eu-repo/semantics/altIdentifier/doi/10.3390/s23052497
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Mots-clés
machine learning BIM H-BIM artificial intelligence point cloud classification digital heritage cultural heritage 3D models scan-to-BIM semantic segmentationSujets proches
Knowledge, Classification of Intellectronics AI (Artificial intelligence) Artificial thinking Electronic brains Intelligent machines Intelligence, Artificial Machine intelligence Thinking, Artificial IACiter ce document
Valeria Croce et al., « H-BIM and Artificial Intelligence: Classification of Architectural Heritage for Semi-Automatic Scan-to-BIM Reconstruction », HAL-SHS : architecture, ID : 10.3390/s23052497
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Résumé
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.