Binary damage classification of built heritage with a 3D neural network

Preservation and restoration of historical buildings are important tasks and require accurate assessment of structural damage. However, human and economic efforts are limited, therefore, it is important to automatize such a task. While there are several studies that can identify damages in the buildings, the majority use images to identify slight or moderate damage and only a few can detect severe or very severe damages (where the structure has been affected). This gap is partially due to two challenges: (i) the uniqueness of the built heritage limits the number of available datasets, and (ii) to deal with detailed 3D representations requires high computational resources. To address these challenges, this paper proposes an automatic classification of the structural state of the built heritage, considering severe or very severe degrees of damage. Due to the limited number of available models, this work uses point clouds converted to voxel map representations, improving the generalization of the method. To achieve the classification, a 3D Convolutional Neural Network (CNN) with five layers is adapted and trained under supervised learning with a minimal dataset. The training dataset contains 130 built heritage structures and was specifically created by authors using CAD tools. The trained 3D CNN was tested on two real-world buildings: (1) a Posa chapel with very severe damage belonging to the Natividad former convent at Tepoztlán, Morelos, Mexico. The convent has been recognized as a UNESCO World Heritage site since 1994. (2) The Kukulkán temple with no severe damage. This is one of the 7 Wonders of the World, located at the archeological site of Chichén Itzá at Yucatán, Mexico. This site has been recognized as a UNESCO World Heritage since 1988. The results show that the proposed method successfully classifies the structural state of the heritage buildings. Key findings include: (i) low-resolution voxel maps effectively preserve essential structural features for supervised learning while requiring a moderate number of examples, and (ii) the approach is scalable for other unique heritage structures. This method provides a viable tool for automated damage assessment, supporting preservation and restoration efforts for cultural heritage.