The Yellow River Basin, a cradle of Chinese civilization, hosts traditional riverside villages that embody millennia of cultural and ecological heritage. Despite their significance, rapid urbanization and homogeneous rural development have precipitated landscape homogenization and cultural erosion, threatening these villages’ spatial integrity and cultural capital. Current research predominantly focuses on qualitative characterization of architectural heritage, neglecting quantitative assessments of agroecological synergies and systematic resource endowment analysis. This oversight limits the development of proactive conservation strategies tailored to the integrated cultural–ecological value of these villages, hindering their sustainable revitalization within China’s broader Yellow River Basin high-quality development strategy. Here, we develop a comprehensive framework integrating landscape characterization, value assessment, and conservation strategies for traditional villages along Shaanxi’s Yellow River. Using GISs 10.2 multi-criteria analysis, and field surveys, we construct a hierarchical landscape database and evaluate villages across cultural, ecological, and socio-economic dimensions. Our results reveal distinct spatial patterns, with 65% of historical structures clustered in village cores, and identify four landscape zones requiring targeted conservation. High-value villages (e.g., Yangjiagou) exhibit strong cultural preservation and ecological resilience, while lower-scoring villages underscore urgent intervention needs. We propose multi-scale protection strategies, including regional clustering and village-level tailored approaches, to balance conservation with sustainable development. This study fills the critical gap in systematic resource endowment evaluation by demonstrating how integrated cultural–ecological metrics can guide proactive conservation. Our framework not only safeguards tangible and intangible heritage but also aligns with national strategies for rural revitalization and ecological protection. By bridging methodological divides between qualitative and quantitative approaches, this research offers a replicable model for sustainable rural development in ecologically sensitive cultural landscapes globally, advancing the field beyond static preservation paradigms toward dynamic, evidence-based planning.

The conservation of traditional villages has shifted from isolated site-by-site protection to regional collaboration, and exploring pathways for their sustainable development has become a key focus of research. Existing research still falls short in areas such as the integration of heritage value into decision-making mechanisms and the establishment of systematic conservation frameworks, leading to prominent issues of isolated conservation and homogeneous development. Taking traditional villages in Yanchuan County, China, as a case study, this research aims to establish a clustered conservation system and achieve a transition towards networked collaborative governance. The study utilised field surveys and literature review to establish a database and systematically catalogue heritage resources; it combined the Analytic Hierarchy Process (AHP) and the Delphi method to construct a value evaluation system and identify distinctive features; and it integrated cluster theory with GIS spatial analysis to construct a clustered conservation framework across three dimensions: classification and grading, symbiotic models, and the overall spatial pattern. The results indicate that: (1) the spatial distribution of villages in Yanchuan County is uneven, and the villages themselves exhibit significant homogeneity in their characteristics; (2) core characteristics include Loess culture, cave dwellings and revolutionary heritage sites, with comprehensive scores ranging from 0.4437 to 0.9116; these are classified into three protection levels, identifying five categories of villages of value. (3) Five major cluster zones were delineated based on resource and spatial characteristics. By integrating river basins and transport corridors, a comprehensive protection framework of ‘one belt, two wings, two centers and five zones’ was established, alongside three types of cluster symbiosis models, thereby achieving regional resource integration and enhancing collaborative efficiency. The cluster-based protection system proposed in this study can effectively address the challenges facing the conservation and development of traditional villages, providing a feasible solution for regional collaborative protection, and holds practical significance for cultural heritage management and sustainable development.

Cultural heritage value is essential for heritage protection and utilization. However, existing evaluation methods remain subjective and labor-intensive. To address these challenges, we propose the Vision Transformer-based Heritage Value Evaluation (ViT-HVE) model, the first deep learning-based model for heritage value evaluation. Taking heritage sites along the Yellow River in Shaanxi as a case study, we construct a ten-dimensional value system using Latent Dirichlet Allocation (LDA) and establish the first Cultural Heritage Value Recognition (CHVR) dataset. To enable quantitative evaluation, we introduce the Top-k Heritage Value Weighting (TK-HVW) method, which derives value weights by extracting and normalizing probability distributions across value categories. Extensive experiments demonstrate that ViT-HVE outperforms existing state-of-the-art methods, achieving 0.890 Precision, 0.887 F1-Score, and 0.889 Accuracy. Furthermore, the predicted value distributions show strong alignment with expert evaluations, substantiating the reliability of our method and highlighting its potential for cultural heritage value evaluation and decision-making.

Cultural heritage value serves as a critical foundation for guiding the protection and utilization of cultural heritage. However, existing methods for evaluating cultural heritage value face several inherent limitations, including evaluator subjectivity, high resource demands, and a lack of timely and objective evaluation capabilities. To overcome these challenges, we present the first application of deep learning to heritage value evaluation through our proposed Vision Transformer-based Heritage Value Evaluation model (ViT-HVE). This novel framework represents a breakthrough in automated cultural heritage analysis, enabling both visual recognition and quantitative evaluation of heritage values through an end-to-end deep learning approach. In this paper, the cultural heritage of the Yellow River in Shaanxi province is selected as a case study. A ten-dimensional value system characterizing these cultural heritage is extracted using the Latent Dirichlet Allocation (LDA) topic model. Based on this, we developed the first Cultural Heritage Value Recognition (CHVR) dataset in this domain. To facilitate the quantitative evaluation of cultural heritage values, we further introduce a Top-k Heritage Value Weighting (TK-HVW) method, which performs value weighting by extracting and normalizing the probability distributions over heritage value categories. To evaluate the effectiveness of the proposed method, we conduct a comparative analysis between ViT-HVE and five state-of-the-art deep learning models, and validate the accuracy of the model’s weighted outputs using expert evaluation. Furthermore, three distinct fine-tuning strategies are systematically investigated to enhance the performance of our model on the CHVR dataset. Experimental results indicate that ViT-HVE achieves the best performance in heritage value recognition tasks, and our proposed weighting method shows a high degree of consistency with expert evaluation results. The ViT-HVE evaluation method significantly enhances the objectivity and accuracy of cultural heritage recognition and evaluation, substantially reducing the cost and workload of related tasks, thereby offering scientific methodological support and practical guidance for the protection and utilization of cultural heritage.

Maintaining rice terraces as living agricultural heritage requires distinguishing interruptions of paddy production from changes in settlement fabric, yet evidence on associated factors remains limited in the Qinba transition zone. This study compared terrace abandonment, paddy-to-dryland conversion, and composite change in the settlement architectural landscape in five villages of the Fengyan Ancient Terraces using 260 household questionnaires, 189 changed-plot records, GIS, and binary logistic models. Overall, 77 (29.62%) experienced abandonment, 65 (25.00%) dryland conversion, and 198 (76.15%) composite settlement change; 31 dwellings (11.92%) were vacant or physically deteriorated. A higher share of farmland served by gravity-fed irrigation was associated with lower odds of abandonment and conversion, whereas greater maximum walking time to cultivated plots was associated with higher odds of abandonment. Core-area location showed no stable association with either production outcome, and the exploratory settlement model was not statistically significant. Because the cross-sectional models identify associations rather than motives or causal sequences, the results do not establish that irrigation deterioration caused conversion. By distinguishing cessation of cultivation, adaptive crop conversion, and settlement change, this study identifies functional irrigation continuity and site-specific accessibility as more informative conservation targets than static boundaries alone, while supporting context-sensitive settlement renewal.

This study addresses the critical gap in designing child-friendly school commuting routes in mountainous cities like Chongqing, where steep terrain and complex infrastructure pose unique challenges to children’s safety and accessibility. Combining Importance-Performance Analysis (IPA) and the Kano model, we systematically evaluated 19 design elements across safety, interest, and convenience dimensions through comprehensive field research and surveys involving 611 participants. Our analysis identified emergency signage systems, street greening, and parent pick-up points as pivotal optimization factors, while revealing significant improvement needs for vehicle speed limits and anti-slip pavement. The findings demonstrate how context-specific, evidence-based design solutions can effectively enhance children’s commuting experiences in challenging urban environments, providing urban planners with a scientifically-grounded framework that adapts child-friendly principles to mountainous contexts. This research advances the field of child-friendly urban planning by bridging the current theoretical-practical divide and offering actionable strategies tailored to geographically constrained cities.