Exploring the Efficacy of CNN and SVM Models for Automated Damage Severity Classification in Heritage Buildings

Numerous Heritage buildings are critical cultural assets susceptible to damage and deterioration. In this study, we explored machine learning techniques for the automated assessment of damage severity in heritage buildings. Specifically, this study has trained the Convolutional Neural Networks (CNN) and Support Vector Machine (SVM) on a dataset of 4,500 images of heritage buildings, each with a resolution of 224x224x3 pixels. The results demonstrate that both CNN and SVM can effectively classify damage severity levels in heritage buildings, with CNN showing slightly better overall performance. This study also found that both the models' performance improved as the damage severity remain increased. These findings suggest automated damage severity assessment using machine learning as a good heritage building management and preservation approach. Further research is needed to explore the feasibility of implementing this approach in real-time and to address potential ethical and technical challenges.