# Historic Built Environment Assessment and Management by Deep Learning Techniques: A Scoping Review
## 5 Conclusions  
The proposed scoping review aimed to provide an overview of the applications of DL architectures for classifying the decay phenomena of the HBE, focusing on the significant implications for decision-making in the built heritage conservation and management. To achieve this, documents that reflected the keywords related to the topic were selected, highlighting the innovative nature of the field under examination. The analysis shows a strong growth trend beginning in 2020, with significant development prospects in the coming years. The main topics addressed in the literature focus on the automated identification of surface degradation pathologies and cracks affecting buildings within the HBE, as well as post-disaster damage assessments at a broader scale. The analysis of the reviewed studies was structured to highlight the different segmentation and classification methodologies employed, the architectures developed for specific objectives, and the results obtained in terms of accuracy and precision. This approach aims to highlight the potential and limitations of each experiment, providing guidance for future research and developments based on the specific characteristics of the cases under examination. The literature reveals significant progress in the efficiency of DL-based models applied to images, offering increasingly accurate assessments of damage affecting heritage at both the building and district scales, with the potential for monitoring changes over time. The integration of DL models with increasingly advanced surveying tools enables remote observation and decay mapping activities, reducing the need for direct on-site presence, even in inaccessible or hazardous locations. Furthermore, the advantages of applying automated degradation identification systems to data acquired through photogrammetric surveys are evident. These systems convert image data into valuable qualitative and quantitative information, providing a comprehensive and detailed understanding of the spatial localization and extent of pathologies. Another significant benefit is the large amount of two-dimensional data that this approach generates, which are essential for image labeling and model training phases. The potential for implementing DL-based systems directly on mobile devices, capable of real-time monitoring of building conditions, is also noteworthy, as is the use of crowdsourced images that leverage the contributions of multiple users to enrich datasets. These contactless, fast, and cost-effective approaches facilitate the early detection of structural and surface building issues and the evaluation of repair and recovery interventions, ensuring informed decision-making for appropriate maintenance and conservation strategies. In this context, the potential to integrate DL models within post-disaster heritage management practices is underscored, offering significant improvements in damage assessment operations and contributing to reducing response times in emergencies. In conclusion, several challenges and development directions remain open in this field. To address issues related to the limited availability of data concerning the diverse topological characteristics typical of the HBE, the creation of open-source databases organized by construction type could significantly accelerate the training and prediction phases for specific case studies, enabling the accurate localization and measurement of degradation. This approach would be fully aligned with the primary goal of the research: minimizing survey, diagnosis, and monitoring times, while ensuring precise measurement data to define appropriate intervention measures. Furthermore, the implementation of DL models in interoperable environments remains limited. Such environments could merge qualitative and quantitative results with data from other types of integrable diagnostic analyses and sensor systems.