Measuring Sense of Place Continuity in traditional townscape through machine learning: an AI-enhanced method for water-town heritage

This study proposes a multidimensional framework for measuring sense of place continuity in traditional settlements—a quality defined as the persistence of place identity cues that enable recognition and meaning making amid urban transformation. Under rapid urbanization, sense of place in traditional townscape is increasingly at risk of erosion. As a form of “living heritage”, it plays a vital role in sustaining social interaction and cultural memory. By leveraging machine learning algorithms with large language models, the study develops an AI-enhanced methodology to advance the computational understanding of “place”, enabling both diagnosis of current conditions and evaluation of continuity. Jiangnan water-town was selected as the study area known for its strong place identity. The framework utilizes multi-source urban data and develops a series of python-based tools to extract spatial, visual, and semantic features using deep convolutional models and natural language processing techniques. These are then compared against reference profiles derived from preserved environments to assess the continuity of sense of place. This study offers a novel pathway for quantifying previously intangible qualities, supporting data-informed urban design and assessment in the context of urbanization. It also contributes to a theoretical reconsideration of authenticity in place-making, shifting emphasis from formal imitation to continuity rooted in local context and lived experience.