Toward Generalized Multi-Typological Classification of Cultural Heritage: A Random Forest Approach
Year: 2026
Type: article
Abstract: . The increasing adoption of point clouds in the digital documentation of Cultural Heritage (CH) has made three-dimensional semantic segmentation a key step for data interpretation and analysis. In this context, Deep Learning (DL) approaches have demonstrated high performance, albeit at the cost of substantial computational requirements and the need for large annotated datasets. Within this framework, the present study investigates the potential of leveraging a traditional supervised Machine Learning (ML) approach - Random Forest (RF) - through targeted optimization of training and validation procedures. To this end, the RF_CHC (Random Forest for Cultural Heritage Classification) model is proposed. Aimed at improving accuracy and, in particular, generalization capability in the semantic classification of architectural CH point clouds, RF_CHC integrates statistical hyperparameter calibration through the adoption of cross-validation procedures. The performance of RF_CHC was evaluated and compared with literature models (RF4PCC and optimized RF4PCC), demonstrating improved classification consistency and greater robustness across heterogeneous datasets, while highlighting the potential of an optimized ML-based approach as a competitive or complementary solution to currently prevalent DL models in the CH domain.
Authors: Giuseppe Antuono, Valeria Cera, Daniela Ciarlo
Institutions: University of Naples Federico II, University of Salento
Language: English
Subfield: Geology
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7128736320
FWCI:
Cites:
Cited by:
Related to: