As economic growth and societal shifts reshape urban environments, cultural and creative industrial parks are emerging as vital contributors to sustainable urban development. The design of these landscapes plays a pivotal role in enhancing user satisfaction, increasing spatial attractiveness, and promoting eco-friendly urban practices. This study examines visitor landscape perception preferences in the Textile and Garment Cultural and Creative Industrial Park, located in Haizhu District, Guangzhou, through a novel methodology combining user-generated content (UGC), deep learning models, outdoor electrodermal activity (EDA) measurements, and questionnaire surveys. The UGC-based landscape recognition model achieved an accuracy of 86.8% and was validated against user preferences captured through questionnaires. Results demonstrate that visitors prefer areas featuring cultural landmarks and natural elements, while spaces dominated by human activity and transportation infrastructure are less favored. Key landscape elements, such as signage, thematic sculptures, brand logos, and trees, were identified as highly preferred features within the park. While EDA experiments revealed significant variations in physiological responses across different spatial settings, no strong correlation was observed between EDA indicators and subjective questionnaire scores. This integrative approach enables a comprehensive, objective assessment of landscape perception, providing a data-driven, user-centered framework for improving landscape design in cultural and creative industrial parks.

The rapid speed of urbanization in modern cities has led to various environmental challenges impacting human activities, livelihoods, and comfort. One of these effects is the urban heat island, which describes the increase in temperature in an urban area resulting from the replacement of natural surfaces with concrete, buildings, and other structures that absorb and retain heat. Variations in individual perception and adaptative ability present additional challenges when trying to ensure outdoor comfort and require advanced measuring instruments and simulation tools to accurately predict a broad range of related variables. In this study, we investigated three different types of blue–green spaces (six in total) on the campus of Guangzhou University, focusing on their distinct layouts. The aim was to evaluate these spaces’ microclimate and sunlight intensity conditions by quantifying several environmental factors. Subjective comfort assessments and objective physiological parameter measurements were conducted using questionnaires and biosensors, respectively. The results revealed the following: (1) Different types of blue–green spaces exhibit distinct microclimate and visual environment characteristics, and while similar patterns emerged, certain environmental parameters revealed important differences. (2) There is a significant association between individuals’ thermal and visual comfort in blue–green spaces and multiple environmental factors. (3) Linear regression analysis demonstrated the strong predictive capabilities of skin conductance indicators (Rsc, SCR, and nSCR) in assessing individuals’ outdoor visual–thermal comfort levels, with R2 exceeding 0.5, indicating high accuracy. These findings provide valuable insights and references for urban planners and designers seeking to enhance the visual and thermal aspects of sustainable landscapes on campuses as well as in other outdoor environments.

With the advancement of resilience concepts, enhancing resilience capacity has become an effective approach to addressing rainwater and flooding issues. Most rural planning and construction efforts adopt urban planning models from economically developed regions, often leading to surface hardening, which subsequently causes drainage difficulties and severe surface water accumulation during the rainy season. In contrast, traditional Lingnan villages, exemplified by Guangdong’s Changqi Ancient Village, continue to function normally in flood-prone areas, suggesting that their water management knowledge merits investigation. Previous research on rainwater management in traditional Chinese villages has predominantly been qualitative, lacking scientific data support. This study employs an eco-social resilience perspective, combining field surveys and interviews with villagers, and utilizes the SWMM (Storm Water Management Model) software to conduct both qualitative and quantitative analyses of Changqi Ancient Village. The findings reveal the following: (1) The SWMM effectively quantifies rainwater and flood management in traditional villages. (2) From an ecological resilience perspective, the village’s geographical location is crucial. The topography, along with a rainwater regulation system comprising rivers, ponds, ditches, and permeable pavements, significantly influences the village’s drainage performance. (3) From a social resilience perspective, community participation is vital to the long-term stable development of traditional villages. This includes post-disaster collective fundraising by villagers for the restoration of rainwater and flood management facilities, the formulation of village regulations, and the construction and restoration of spiritual sites. (4) From an eco-social resilience perspective, the eco-social resilience system exhibits adaptive cyclical characteristics, where the geographical environment and the local economy significantly shape the ecological spatial patterns of Changqi, while positive interaction between nature and human society ensures the system’s dynamic equilibrium.

Previous morphological studies have confirmed that Canton settlements maintain a stable cultural landscape sequence consisting of ponds, open spaces, ancestral halls, residences, and forests. Villages in the Dongjiang River Basin exhibit an inherent coordination between cultural landscape patterns and rainwater drainage and storage systems, contributing to strong resilience against frequent heavy rainfall events. This study selects Jiangbian Village in Dongguan as a case study and develops a quantitative analysis framework by integrating GIS and SWMM (Storm Water Management Model). Using DEM-derived terrain data and land use interpretation, a hydrological model incorporating ponds, drainage channels, paddy fields, and threshing floors was established. Five levels of functional failure severity and five design rainfall return periods were applied to systematically evaluate hydrological regulation performance. The results show that (1) ponds serve as the core water-storage component of the entire system, and a 25% reduction in their functionality leads to a substantial decline in flood mitigation capacity. Paddy field ridges and drainage channels jointly provide secondary buffering functions, while village boundary structures play a significant role in flood regulation. These landscape elements possess distinct hydrological functions and collectively shape the production, living, and ecological landscapes of the village. (2) Influenced by steep topographic gradients, the village adopts a spatial configuration characterized by horizontal alleys and terraced forms, which enhance transverse drainage and connect ponds through longitudinal channels. This comb-like settlement pattern demonstrates strong adaptation to local terrain conditions. This study reveals the terrain-adaptation characteristics of traditional Canton villages and provides valuable insights for the sustainable conservation of rural cultural landscapes.