Abstract
In recent years, machine learning has made new progress in image and semantic recognition, providing digital platforms for architectural and landscape research. The cultural heritage sites along the Jiangnan Canal show great aesthetic value in their forms, shapes, and structures, which deserve research and preservation with digital technologies. This research collects images of cultural architectural heritage along Jiangnan Canal to create a dataset, including gardens, gates, guild halls, pavilions, pagodas, residences, temples, gates, bridges and street blocks, etc. A model was developed on Google Teachable Machine for building type recognition and classification, and comparative experiments with historical and modern images were conducted to analyze the model’s accuracy. Classification results show the factors that affect the accuracy of the model and further adjustments of the model are proposed. This paper contributes to the application of AI in cultural heritage protection and offers new methods for recognition of historical architecture types. Using digital techniques and platforms, this classifier has the potential to be applied in a wide range of digital design areas and could be further developed for recognizing architecture morphology.
Presenters
Shengdan YangStudent, Ph.D., Tsinghua University, Beijing, China Yan Huang
Professor, Environmental Design, Academy of Arts & Design, Tsinghua University, Beijing, China
Details
Presentation Type
Paper Presentation in a Themed Session
Theme
2023 Special Focus—-New Aesthetic Expressions: The Social Role of Art
KEYWORDS
Machine learning, Cultural heritage, Jiangnan Canal, Classifier model, Building types
