Synthetic Training Datasets for Architectural Conservation: A Deep Learning Approach for Decay Detection

Abstract

Architectural heritage conservation increasingly relies on innovative tools for detecting and monitoring degradation. The study presented in the current paper explores the use of synthetic datasets—namely, rendered images derived from photogrammetric models—to train convolutional neural networks (CNNs) for the automated detection of deterioration in historical reinforced concrete structures. The primary objective is to assess the effectiveness of synthetic images for deep learning training, comparing their performance with models trained on traditional datasets. The research focuses on a significant case study: the parabolic concrete arch of Morano sul Po. Two classification scenarios were tested: a single-class model for structure recognition and a multi-class model for identifying degradation patterns, such as exposed reinforcement bars. The findings indicate that synthetic datasets can effectively support structure identification, achieving results comparable to those obtained with real-world imagery. However, challenges arise in multi-class classification, particularly in distinguishing fine-grained degradation features. This study highlights the potential of artificial datasets in overcoming the limitations of annotated data availability in heritage conservation. The proposed approach represents a promising step toward automating documentation and damage assessment, ultimately contributing to more efficient and scalable heritage monitoring strategies.