User-generated data can reflect various viewpoints and experiences derived from people’s perception outcomes. The perceptual results can be obtained, often by combining subjective public perceptions of the landscape with physiological monitoring data. Accessing people’s perceptions of the landscape through text is a common method. It is hard to fully render nuances, emotions, and complexities depending only on text by superficial emotional tendencies alone. Numerical representations may lead to misleading conclusions and undermine public participation. In addition, the use of physiological test data does not reflect the subjective reasons for the comments made. Therefore, it is essential to deeply parse the text and distinguish between segments with different semantic differences. In this study, we propose a perceptual psychology-based workflow to extract and visualize multifaceted views from user-generated data. The analysis methods of FCN, LDA, and LSTM were incorporated into the workflow. Six areas in Fuzhou City, China, with 12 city parks, were selected as the study object. Firstly, 9987 review data and 1747 pictures with corresponding visitor trajectories were crawled separately on the Dianping and Liangbulu websites. For in-depth analysis of comment texts and making relevant heat maps. Secondly, the process of clauses was added to get a more accurate representation of the sentiment of things based on the LSTM sentiment analysis model. Thirdly, various factors affecting the perception of landscapes were explored. Based on such, the overall people’s perception of urban parks in Fuzhou was finally obtained. The study results show that (1) the texts in terms of ‘wind’, ‘temperature’, ‘structures’, ‘edge space (spatial boundaries)’, and ‘passed space’ are the five most representative factors of the urban parks in Fuzhou; (2) the textual analyses further confirmed the influence of spatial factors on perception in the temporal dimension; and (3) environmental factors influence people’s sense of urban parks concerning specificity, clocking behavior, and comfort feelings. These research results provide indispensable references for optimizing and transforming urban environments using user-generated data.

Fujian Tulous in China are important international architectural heritage sites that reflect precious human cultural heritage. Currently, only a small number of Tulou buildings have been listed as world cultural heritage sites, resulting in a lack of attention and financial support for most Tulou buildings. Thus, it is difficult to effectively renovate and repair Tulou buildings to adapt to modern life, and therefore they are facing the severe challenge of abandonment and desolation. Due to the special conditions of Tulou buildings, there are significant limitations in renovation and repair work, with a number of problems such as the lack of innovative renovations. Therefore, through a problem model analysis of a design system for Tulou renovations, in this study, we adopt the methods of divergent tree, conjugate pair, correlative net, implied system, and split-merge chain analyses in extenics to carry out extension transformation and solve the problem and we verify its feasibility using the example of the Tulou renovation projects in Lantian Village, Longyan City. We explore an innovative methodology for scientific renovation of Tulou buildings, and we establish a design system for Tulou building renovations that enriches and supplements original renovation methods; thus, we provide a basis for the repair and reuse of Tulou buildings, to extend their service life and to realize the sustainable development of Tulou buildings. The research results show that extenics can be implemented in innovative renovations of Tulou buildings, and it is concluded that the essence of achieving sustainable renewal in Tulou building renovations is to solve contradictory problems, including contradictions in conditions, objectives, and design. This study verifies the possibility of applying extenics in the design of Tulou building renovations, makes corresponding contributions to the application of extension methods in the renovation and renewal of Tulou buildings, and also contributes to the renovation, renewal, and protection of other types of architectural heritage sites.

Highway under-bridge areas represent a valuable land resource while simultaneously constituting a sensitive ecological zone. Achieving a balance between its redevelopment and ecological preservation constitutes a critical challenge within the field of ecological engineering. Although prior research has addressed urban elevated underbridge space, investigations specifically focusing on highway underpasses remain limited. The absence of standardized criteria for assessing the suitability of these spaces has resulted in uncoordinated and fragmented utilization. In response, this study proposes a comprehensive evaluation framework that integrates multi-criteria decision-making (MCDM) methodologies with optimized backpropagation neural networks, specifically genetic-algorithm-optimized BP (GA-BP) and particle-swarm-optimization-optimized BP (PSO-BP). The model incorporates indicators spanning physical characteristics, environmental factors, safety considerations, and accessibility metrics, and is applied to an empirical dataset comprising 134 highway bridge underpasses in Fuzhou City. The results indicate that (1) both the GA-BP and PSO-BP models enhance convergence speed and classification accuracy, with the GA-BP model demonstrating superior stability and suitability for classifying underpass suitability; (2) the principal determinants of suitability include traffic accessibility, safety parameters, and spatial relationships with adjacent water bodies and agricultural lands; and (3) underpasses characterized as hub-type, single-sided road-adjacent, and cross-connection configurations exhibit greater potential for redevelopment. This investigation represents the first integration of MCDM and optimized neural network techniques in this context, offering a robust tool to support the scientific planning and ecological conservation of underbridge space environments.

Water environment quality is profoundly driven by a series of landscape characteristics. However, current knowledge is limited to the independent response of water quality to single landscape elements; this has led to poor knowledge of the potential role of structural coupling within landscapes in driving water quality changes, especially in those agroforestry-mixed mountainous watersheds with highly embedded forest-clustered landscapes and abundant headstreams. Given this fact, this study aims to evaluate whether and how the source–sink coupling structure of forest-clustered landscapes systematically drives headstream quality dynamics. We first systematically assessed the association pattern of source and sink structures within forest-clustered landscapes, and then innovatively proposed and constructed a functional framework of source–sink coupling structure of landscapes across 112 agroforestry-mixed mountainous sub-watersheds in Chongqing, China. On this basis, we further evaluated the driving pattern and predictive performance of the source–sink coupling structure of landscapes behind headstream quality dynamics. We report three findings: (1) headstream quality varied across agroforestry-mixed sub-watersheds, mapping out the source–sink structures and functions of landscapes; (2) there was significant functional coordination between source–sink structures of the forest-clustered landscapes, which significantly drove headstream quality dynamics; (3) the structural positioning and differences of the forest-clustered landscapes along the multivariate functional axes directly corresponded to and predicted headstream quality status. These findings together highlight a key logic that the response of water quality dynamics to landscapes is essentially that to the functional coupling between the source–sink structures of landscapes, rather than the simple combination of a single landscape contribution. This is the first study on the landscape–runoff association from the perspective of source–sink structural coupling, which helps to deepen understanding of the correlation mechanism between water dynamics and landscape systems, and provides a new functional dimension to the development of future landscape ecological management strategies from a local to a global scale.

In the context of urban renewal, spaces beneath elevated bridges are gradually becoming important public activity areas, with their microclimate and landscape quality influenced by multiple factors including bridge structure, vegetation configuration, and traffic conditions. This study focuses on 12 typical elevated bridge underpass spaces in Fuzhou and Xiamen, two hot–humid subtropical cities in China, and integrates field environmental monitoring, questionnaire surveys, spatial landscape metrics, and statistical analysis to explore how plant landscape factors are significantly associated with thermal and acoustic conditions and overall comfort levels. Results show the following: (1) Overall comfort refers to the overall subjective evaluation of the elevated bridge underpass environment under combined thermal and acoustic conditions. Thermal and acoustic comfort jointly explained 71.3% of its variance (R² = 0.713), with thermal comfort contributing more strongly. (2) Bridge orientation was associated with differences in shading conditions and microclimatic characteristics, with east–west-oriented elevated bridge underpass spaces generally offering better overall comfort. (3) Green coverage rate, green buffer width, distance from roads, and hard paving proportion were identified as key landscape factors associated with overall comfort. Controlled regression showed significant associations, with green coverage rate showing the strongest positive association with OCV (β = 0.612), whereas hard paving proportion showed the strongest negative association (β = −0.628); the models explained 66.8–68.4% of OCV variance. (4) Appropriate plant landscape configurations were associated with more favorable microclimatic conditions under bridges. Within the studied sites, higher comfort was associated with approximately 40–55% green coverage, 8–15 m green-buffer width, a road distance above 60 m, and a hard paving ratio below 50%. These values should be regarded as context-specific design references derived from the observed regression patterns rather than universal optimal thresholds. This study identifies associations among plant landscape configuration, microclimate, and environmental comfort in elevated bridge underpass spaces based on field evidence from Fuzhou and Xiamen, providing context-specific references for landscape renewal in similar hot–humid subtropical settings.