Heritage Identification of Monument Using Deep Learning

Abstract

Abstract: There are many beautiful and colorful countries in the world which are famous for their culture and heritage. The historical landmarks of such countries convey a great deal through their magnificent structures and rich heritage. The extensive past and legacy of such nations are apparent in the splendid palaces, fortresses, towers, shrines, and cathedrals that are scattered throughout the entirety of their land. We need to protect cultural monuments and landmarks as they are one of the sources of inspiration for future generations. Traditional methods for monument identification often rely on manual inspection or rudimentary computer vision techniques, which can be time-consuming and error-prone. We can provide the best solutions using modern machine learning and deep learning techniques. In our model, we used an approach based on deep learning for the automated identification of heritage monuments from images. We leveraged the power of convolutional neural networks (CNNs) to extract hierarchical features from monument images and train a classifier to recognize different architectural styles and structures. We evaluated our approach on a dataset comprising diverse monument images collected from public archives and online repositories. We applied various pre-processing techniques such as image resizing, normalization of pixel values, and data augmentation. We analyzed and further improved the model’s performance for heritage identification of monuments.