Ancient temple heritage space is a subcategory of integrated spaces with profound religious architecture, culture, and landscape. The temporal and spatial characteristics, spatial layouts, and functionalities of ancient temples are gradually transformed during different periods in their development. However, quantitative topological estimation tools, e.g., space syntax and detailed digital spatial models, have seldom been adopted in related studies on ancient temples. Daming Temple is a typical representative of the revitalization of Buddhism monastic building heritage in China. This research studies the spaces of Daming Temple, Yangzhou City, in three different periods and explores its spatio–temporal characteristics based on two space syntax models, i.e., the angle segment analysis (ASA) model and the visibility map analysis (VGA) model. By multi-step quantitative estimation, changes in the mean depth (MD), mean connectivity, and intelligibility of the temple have been observed. The global spatial structure is thoroughly revealed, which indicates the changes in the ‘temple-residence-garden’ inter-relationship. It is indicated that dynamic spatio–temporal characteristics of the temple have been undergoing changes chronically. Some phenomena are found to be effective in offering reasonable explanations for these changes, i.e., the changes in relationships among spaces, visitors’ pathfinding difficulties, and spatial design techniques. It also found that there are certain correlations between temporal–spatial changes and spatial conservation strategies for building heritages. The case study can provide some valuable references for the conservation, reactivation, and redesign of related historical and cultural building heritage in East Asia.

China’s rapid urbanization has been accompanied by serious urban sprawl. Instead of measuring the physical urban boundaries (PUBs), most of existing studies in China rely on yearbook statistics to describe the growth of urbanized area; therefore, the understanding of the actual form and quantity of urban sprawl are restrained. As the statistical unit is generally at or above the county level, these studies tend to omit the lower-level “larger towns”. This paper discusses the measurement of urban sprawl and compactness using multi-source data on the GIS platform through the case study of the Wen-Tai region in China. GlobeLand30 remote sensing image data, vector road network data, NPP/VIIRS nighttime light data, and points of interest (POIs) data are adopted. The new method enhances the identification of built-up areas in larger towns. Besides, the 2020s’ PUBs of this region, data for 2010 and 2000 are retraced to assess the urban expansion rate, and two approaches are used to discuss the urban growth pattern. Additionally, a compactness model is constructed from four dimensions, i.e., the compactness of external contour, accessibility of road network, land-use intensity, and functional diversity, by which a high-resolution visual analysis tool is created for the provincial government to monitor urban sprawl.

It is widely accepted that permeability is a key attribute for sustainable development, but two puzzles remain unsolved. First, there is no consensus on its measurement. Second, there are still many new urban developments that lack this quality; regardless, it is a well-known urban design principle. This paper reviews different permeability measurements and proposes to categorize them into four quadrants, then compares eleven of them with four sets of abstract forms and four real cases from Southeast China. The two measures rooted in space syntax theory demonstrate their robustness in the abstract forms. In contrast, the area-based measures cannot quantify the differences between places with similar content. In the actual case studies, by comparing the differences between original plans and the real built forms, we suggest that three traps impair their permeability and therefore deviate greatly from the initial design visions, resulting in automobile-oriented travel patterns. The two new measures were used to quantify the different levels of permeability in these four cases. It is concluded that the qualitative urban design principles may fail to be delivered without a quantitative and precise description of urban form. Consistent measures and more empirical cases are needed to reveal the contribution of spatial networks to sustainable development.

Artificial intelligence is reshaping approaches to architectural heritage conservation by enabling a deeper understanding of how people perceive and experience historic built environments. This study employs deep learning and large language models (LLMs) to explore public perceptions of the Qinghefang Historical and Cultural District in Hangzhou, illustrating how AI-driven analytics can inform intelligent heritage management and architectural revitalization. Large-scale public online reviews were processed through BERTopic-based clustering to extract thematic structures of experience, while interpretive synthesis was refined using an LLM to identify core perceptual dimensions including Hangzhou Housing & Residential Choice, Hangzhou Urban Tourism & Culture, Hangzhou Food & Dining, and Qinghefang Culture & Creative. Sentiment polarity and emotional intensity were quantified using a fine-tuned BERT model, revealing distinct affective and perceptual patterns across the district’s architectural and cultural spaces. The results demonstrate that AI-based textual analytics can effectively decode human–heritage interactions, offering actionable insights for data-informed conservation, visitors’ experience optimization, and sustainable management of historic districts. This research contributes to the emerging field of AI-driven innovation in architectural heritage by bridging computational intelligence and heritage conservation practice.