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

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.