H-BIM and Artificial Intelligence: Classification of Architectural Heritage for Semi-Automatic Scan-to-BIM Reconstruction
Article dans une revue avec comité de lecture
Auteur
CROCE, Valeria
261638 Modèles et simulations pour l'Architecture et le Patrimoine [MAP]
366408 University of Pisa [Italy] = Università di Pisa [Italia] = Université de Pise [Italie] [UniPi]
543315 Laboratoire d’Ingénierie des Systèmes Physiques et Numériques [LISPEN]
261638 Modèles et simulations pour l'Architecture et le Patrimoine [MAP]
366408 University of Pisa [Italy] = Università di Pisa [Italia] = Université de Pise [Italie] [UniPi]
543315 Laboratoire d’Ingénierie des Systèmes Physiques et Numériques [LISPEN]
CAROTI, Gabriella
366408 University of Pisa [Italy] = Università di Pisa [Italia] = Université de Pise [Italie] [UniPi]
366408 University of Pisa [Italy] = Università di Pisa [Italia] = Université de Pise [Italie] [UniPi]
PIEMONTE, Andrea
366408 University of Pisa [Italy] = Università di Pisa [Italia] = Université de Pise [Italie] [UniPi]
366408 University of Pisa [Italy] = Università di Pisa [Italia] = Université de Pise [Italie] [UniPi]
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.
Fichier(s) constituant cette publication
- Nom:
- LISPEN_SENSORS_2023_VERON.pdf
- Taille:
- 13.90Mo
- Format:
- Fin d'embargo:
- 2023-08-23
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Article dans une revue avec comité de lectureCROCE, Valeria; CAROTI, Gabriella; DE LUCA, Livio; JACQUOT, Kévin; PIEMONTE, Andrea; VÉRON, Philippe (MDPI, 2021)This work presents a semi-automatic approach to the 3D reconstruction of Heritage-Building Information Models from point clouds based on machine learning techniques. The use of digital information systems leveraging on ...
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Ouvrage scientifiqueCROCE, Valeria; GABRIELLA, CAROTI; DE LUCA, L.; PIEMONTE, A; VERON, Philippe (FrancoAngeli srl, 2021)L’applicazione dell’Intelligenza Artificiale (IA) ai beni architettonici introduce promettenti evoluzioni nel settore del Disegno: tecniche più automatizzate di segmentazione semantica possono essere estese al modello ...
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Communication avec acteAbstract. Research in the field of Cultural Heritage is increasingly moving towards the creation of digital information systems, in which the geometric representation of an artifact is linked to some external information, ...
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Article dans une revue avec comité de lectureNowadays most digital reconstructions in architecture and archeology describe buildings heritage as awhole of static and unchangeable entities. However, historical sites can have a rich and complex history, sometimes full ...
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