The tangible patterns of urban heritage sites are composed of complex components, and their interaction is involved in the process of formation and transformation. The past of cities also partially survives in the structure of the settlement, even if many buildings are demolished or significantly transformed. In this study, we introduce a model based on the integration of urban morphology, deep learning, and artificial intelligence methods for exploring the tangible patterns of urban heritage areas at different levels of scale. The proposed model is able to define and recognize the characteristics of the basic elements of urban forms at different resolution levels and reveal the patterns of the heritage. The basic principle of the model is the analysis of urban heritage sites located in different parts of the historical city center of Istanbul. We first define the relationship patterns and complexity levels, and form the characteristics of the urban form by using geographic information systems (GIS), based on the cartographic and contemporary maps. We then employ deep-learning-based convolutional neural networks (CNNs) for automatic segmentation, using OpenCV and NumPy in Python to extract streets and blocks on both historical and contemporary map sources. Based on the results integrated with human intelligence and the CNNs model, we finally generate several prompts for AI for better reasoning in the process of pattern recognition. Our results reveal that this integration increases GPT-4o’s assumptions in the pattern recognition process and, thus, it is able to reveal similar results to those obtained from the form features with different levels of specificity that are interdependent and complementary to human assessments.
Sarihan et al. (Thu,) studied this question.