Historic Built Environment Assessment and Management by Deep Learning Techniques: A Scoping Review

Abstract

Recent advancements in digital technologies and automated analysis techniques applied to Historic Built Environment (HBE) demonstrate significant advantages in efficiently collecting and interpreting data for building conservation activities. Integrating digital image processing through Artificial Intelligence approaches further streamlines data analysis for diagnostic assessments. In this context, this paper presents a scoping review based on Scopus and Web of Science databases, following the PRISMA protocol, focusing on applying Deep Learning (DL) architectures for image-based classification of decay phenomena in the HBE, aiming to explore potential implementations in decision support system. From the literature screening process, 29 selected articles were analyzed according to methods for identifying buildings’ surface deterioration, cracks, and post-disaster damage at a district scale, with a particular focus on the innovative DL architectures developed, the accuracy of results obtained, and the classification methods adopted to understand limitations and strengths. The results highlight current research trends and the potential of DL approaches for diagnostic purposes in the built heritage conservation field, evaluating methods and tools for data acquisition and real-time monitoring, and emphasizing the advantages of implementing the adopted techniques in interoperable environments for information sharing among stakeholders. Future challenges involve implementing DL models in mobile apps, using sensors and IoT systems for on-site defect detection and long-term monitoring, integrating multimodal data from non-destructive inspection techniques, and establishing direct connections between data, intervention strategies, timing, and costs, thereby improving heritage diagnosis and management practices.