Next Article in Journal
On the Detection and Long-Term Path Visualisation of A-68 Iceberg
Next Article in Special Issue
Documentation of Complex Environments Using 360° Cameras. The Santa Marta Belltower in Montanaro
Previous Article in Journal
Extraction and Analysis of Finer Impervious Surface Classes in Urban Area
 
 
Article

From the Semantic Point Cloud to Heritage-Building Information Modeling: A Semiautomatic Approach Exploiting Machine Learning

1
Department of Civil and Industrial Engineering, ASTRO Laboratory, University of Pisa, 56122 Pisa, Italy
2
Modèles et Simulations pour l’Architecture et le Patrimoine, UMR 3495 CNRS/MC, 13402 Marseille, France
3
MAP-ARIA, UMR 3495 CNRS/MC, ENSA Lyon, 69120 Lyon, France
4
LISPEN EA 7515, Arts et Métiers ParisTech, 13617 Aix-en-Provence, France
*
Author to whom correspondence should be addressed.
Academic Editor: Domenico Visintini
Remote Sens. 2021, 13(3), 461; https://doi.org/10.3390/rs13030461
Received: 17 December 2020 / Revised: 21 January 2021 / Accepted: 23 January 2021 / Published: 28 January 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 three-dimensional (3D) representations in architectural heritage documentation and analysis is ever increasing. For the creation of such repositories, reality-based surveying techniques, such as photogrammetry and laser scanning, allow the fast collection of reliable digital replicas of the study objects in the form of point clouds. Besides, their output is raw and unstructured, and the transition to intelligible and semantic 3D representations is still a scarcely automated and time-consuming process requiring considerable human intervention. More refined methods for 3D data interpretation of heritage point clouds are therefore sought after. In tackling these issues, the proposed approach relies on (i) the application of machine learning techniques to semantically label 3D heritage data by identification of relevant geometric, radiometric and intensity features, and (ii) the use of the annotated data to streamline the construction of Heritage-Building Information Modeling (H-BIM) systems, where purely geometric information derived from surveying is associated with semantic descriptors on heritage documentation and management. The “Grand-Ducal Cloister” dataset, related to the emblematic case study of the Pisa Charterhouse, is discussed. View Full-Text
Keywords: heritage; 3D survey; H-BIM; point cloud; classification; semantic annotation; machine learning; Random Forest; laser scanning; photogrammetry heritage; 3D survey; H-BIM; point cloud; classification; semantic annotation; machine learning; Random Forest; laser scanning; photogrammetry
Show Figures

Graphical abstract

MDPI and ACS Style

Croce, V.; Caroti, G.; De Luca, L.; Jacquot, K.; Piemonte, A.; Véron, P. From the Semantic Point Cloud to Heritage-Building Information Modeling: A Semiautomatic Approach Exploiting Machine Learning. Remote Sens. 2021, 13, 461. https://doi.org/10.3390/rs13030461

AMA Style

Croce V, Caroti G, De Luca L, Jacquot K, Piemonte A, Véron P. From the Semantic Point Cloud to Heritage-Building Information Modeling: A Semiautomatic Approach Exploiting Machine Learning. Remote Sensing. 2021; 13(3):461. https://doi.org/10.3390/rs13030461

Chicago/Turabian Style

Croce, Valeria, Gabriella Caroti, Livio De Luca, Kévin Jacquot, Andrea Piemonte, and Philippe Véron. 2021. "From the Semantic Point Cloud to Heritage-Building Information Modeling: A Semiautomatic Approach Exploiting Machine Learning" Remote Sensing 13, no. 3: 461. https://doi.org/10.3390/rs13030461

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop