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Article

A Multilevel Multiresolution Machine Learning Classification Approach: A Generalization Test on Chinese Heritage Architecture

by
Kai Zhang
1,
Simone Teruggi
1,*,
Yao Ding
2 and
Francesco Fassi
1
1
3D Survey Group, ABC Department, Politecnico di Milano, Via Ponzio 31, 20133 Milano, Italy
2
Institute of Architectural History and Theory, Tianjin University, Road Weijin 92, Tianjin 300072, China
*
Author to whom correspondence should be addressed.
Heritage 2022, 5(4), 3970-3992; https://doi.org/10.3390/heritage5040204
Submission received: 16 October 2022 / Revised: 25 November 2022 / Accepted: 1 December 2022 / Published: 6 December 2022
(This article belongs to the Special Issue 3D Virtual Reconstruction and Visualization of Complex Architectures)
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Abstract

In recent years, the investigation and 3D documentation of architectural heritage has made an efficient digitalization process possible and allowed for artificial intelligence post-processing on point clouds. This article investigates the multilevel multiresolution methodology using machine learning classification algorithms on three point-cloud projects in China: Nanchan Ssu, Fokuang Ssu, and Kaiyuan Ssu. The performances obtained by extending the prediction to datasets other than those used to train the machine learning algorithm are compared against those obtained with a standard approach. Furthermore, the classification results obtained with an MLMR approach are compared against a standard single-pass classification. This work proves the reliability of the MLMR classification of heritage point clouds and its good generalizability across scenarios with similar geometrical characteristics. The pros and cons of the different approaches are highlighted.
Keywords: cultural heritage; point cloud; classification; machine learning; Chinese architecture cultural heritage; point cloud; classification; machine learning; Chinese architecture

Share and Cite

MDPI and ACS Style

Zhang, K.; Teruggi, S.; Ding, Y.; Fassi, F. A Multilevel Multiresolution Machine Learning Classification Approach: A Generalization Test on Chinese Heritage Architecture. Heritage 2022, 5, 3970-3992. https://doi.org/10.3390/heritage5040204

AMA Style

Zhang K, Teruggi S, Ding Y, Fassi F. A Multilevel Multiresolution Machine Learning Classification Approach: A Generalization Test on Chinese Heritage Architecture. Heritage. 2022; 5(4):3970-3992. https://doi.org/10.3390/heritage5040204

Chicago/Turabian Style

Zhang, Kai, Simone Teruggi, Yao Ding, and Francesco Fassi. 2022. "A Multilevel Multiresolution Machine Learning Classification Approach: A Generalization Test on Chinese Heritage Architecture" Heritage 5, no. 4: 3970-3992. https://doi.org/10.3390/heritage5040204

APA Style

Zhang, K., Teruggi, S., Ding, Y., & Fassi, F. (2022). A Multilevel Multiresolution Machine Learning Classification Approach: A Generalization Test on Chinese Heritage Architecture. Heritage, 5(4), 3970-3992. https://doi.org/10.3390/heritage5040204

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