Damage Classification in Historical Buildings Through Transfer Learning Approaches

Abstract

Historical buildings are important cultural assets that reflect the identity of cities and preserve the collective memory of societies. However, these structures are increasingly exposed to environmental degradation and human-induced impacts, making their systematic documentation and condition assessment essential for effective conservation strategies. Recent advances in artificial intelligence have provided powerful tools for image-based analysis in the field of heritage preservation. In particular, transfer learning enables the adaptation of pre-trained deep learning models to domain-specific tasks with limited labeled data. In this study, a deep transfer learning-based framework is proposed for automatic damage detection and classification in historical buildings. A new near-balanced dataset of 20,000 images spanning six deterioration categories was developed and made publicly available. Ten convolutional neural network and transformer architectures pre-trained on ImageNet were systematically compared under a unified Bayesian optimization protocol. Experimental results on a held-out test set show that EfficientNetB3 achieves the highest classification accuracy (97.65%), while AlexNet obtains the lowest performance (83.89%); the validation set was used exclusively for hyperparameter tuning. The results demonstrate that transfer learning-based models can effectively identify visually observable deterioration patterns and provide reliable support for automated documentation processes. The proposed framework contributes to the development of data-driven decision-support tools for digital documentation and condition assessment in heritage conservation.