Along with global climate change and the worldwide heat island phenomenon, developing climatic methods and planning practices for the benefit of thermal comfort is of increasing interest. Studies have focused on urban streets, studying the aspect ratio, the orientation, street vegetation patterns, etc. and how they affect thermal comfort. While the role of vegetation is undeniable, this paper asks the question whether the effects of a tree configuration does not vary under different street configurations, and if yes, how to select tree species and determine their appropriate layout. Here, an analytical framework is proposed to test the different tree configurations (changing one variable at a time) with the least favorable street configuration. It is confirmed that the east–west oriented streets are the least favorable cases and denser tree canopies are better for cooling. The interval between the trees are observed to have an optimal effect when it is equal to the crown width at maturity. Furthermore, the results show that the heat mitigation rate of a tree configuration is not linearly improved by the Aspect Ratio (AR). In the case of Shantou city, the improvement of thermal comfort slows down when the AR reaches 1.5 while Mangifera indica planted with 10 m intervals is recommended among the common street-tree species. Other species could be used also, but should meet the requirements of the canopy density and the interval of layout. The paper does not consider other configuration options such as asymmetrical cases of street geometry and one-side or axial tree planting, etc., but the framework allows for adding such options and simulating thermal comfort for a greater number of scenarios.

In architectural heritage conservation, fragmented data practices and heterogeneous formats hinder knowledge extraction, limiting the translation of raw data into actionable conservation insights. This study proposes a knowledge-centric framework integrating smart data methodologies to bridge this gap. The framework synergizes Heritage Building Information Modeling (HBIM), semantic knowledge graphs, and knowledge bases, prioritizing three interconnected dimensions: geometric digitization through 3D laser scanning and parametric HBIM reconstruction, semantic enrichment of historical texts via NLP and rule-based entity extraction, and knowledge graph-driven discovery of spatiotemporal patterns using Neo4j and ontology mapping. Validated through dual case studies—the Historical Educational Sites in South China (humanistic narratives) and the Dong ethnic drum towers (structural logic)—the framework demonstrates its capacity to automate knowledge generation, converting 20.5 GB of multi-source data into 2652 RDF triples that interconnect 1701 nodes across HBIM models and archival records. By enabling real-time visualization of semantic relationships (e.g., educator networks, mortise-and-tenon typologies) through graph queries, the system enhances interdisciplinary collaboration. Furthermore, the proposed smart data framework facilitated the generation of domain-specific knowledge through systematic data valorization, yielding actionable insights for architectural conservation practice. This research redefines conservation as a knowledge-to-action paradigm, where smart data methodologies unify tangible and intangible heritage values, fostering data-driven stewardship across cultural, historical, and technical domains.

The historic landscape and the value of the ancient city in the stock era present a diversified and mixed problem; as such, this study explores a quantifiable spatial correlation method for landscape layering characteristics and value space, in order to provide support for the urban renewal paths that integrate historical and contemporary needs. Taking as an example Chaozhou Ancient City, a renowned historical and cultural city in China, this study draws on the theory of historical urban landscape layering and comprehensively uses historical graphic interpretation, GIS spatial quantitative analysis, the single-land-use dynamic degree model, the Analytic Network Process, and the Delphi method to quantitatively analyze and evaluate the landscape layering characteristics and value space of the ancient city. Meanwhile, it explores the relationship between the historical landscape layering characteristics and value space of ancient cities using the spatial autocorrelation model and the coupling coordination modulus model. The key findings are as follows: (1) The high-layer space (66.1%) and high-value space (31.1%) of the historic landscape of Chaozhou Ancient City show significant mismatch and imbalance. Spatially, layer spaces increase from the city center toward the periphery, whereas value spaces decrease from the center outward, demonstrating marked spatial heterogeneity. (2) The layer–value space shows a spatial distribution of agglomeration, with Moran’s I index values of 0.2712 and 0.6437, respectively. The agglomeration degree of the value space is much higher than that of the layer space, and both show significant non-equilibrium and associative coupling. (3) Coupling coordination: basically balanced (D = 0.56) indicates a transition toward a more integrated state, although 48% of the region remains in a state of severe dysfunction, mainly consisting of two types of spaces: “high-layer–high-value” and “low-layer–low-value.” These two dysfunctional types should be prioritized in future conservation and renewal strategies. This study provides a more comprehensive quantitative analysis path for identifying and evaluating the landscape layer–value space of the ancient city, providing visualization tools and decision-making support for the future protection and renewal of Chaozhou Ancient City and the declaration of the World Heritage.

Cultural routes, an important concept in heritage conservation, represent an innovative paradigm that is reshaping the contemporary trajectory of cultural heritage research. The Nanling Corridor satisfies the four core criteria for cultural routes—temporal continuity, spatial distribution, cross-cultural attributes, and specific historical functional roles—and stands as a paradigmatic indigenous cultural route in China. Focusing on the Hunan–Guangdong Ancient Road—a core segment of the Nanling Corridor—this study integrates historical document analysis, representative sample field surveys, and a historical restoration method to systematically classify and characterize the ancient road’s landscape features. The study findings indicate that the Hunan–Guangdong border region within the Nanling area is endowed with a distinctive natural geographical setting and a complex socio-cultural context. Against this background, landscape elements are categorized into two primary clusters: those directly associated with the ancient road and those indirectly linked to it. The directly associated landscapes are further subdivided into four categories: the cross-territorial route, meso-scale hubs enabling land–water transfer, widely distributed micro-scale ancillary facilities, and intangible engineering techniques. The indirectly associated landscapes encompass four dimensions—military defense, population migration, commercial trade, and religious practice—each demonstrating unique landscape attributes while sharing homologous formative mechanisms. This study aims to provide a China-focused research reference for the international theory of cultural routes through the systematic study of the landscapes along the Hunan–Guangdong Ancient Road within the Nanling Corridor.