Rural health governance is an important part of low-carbon green life, which is also related to the sustainable development and population health project in rural areas. Based on the survey data of 2343 rural residents in China, this study adopted a binary logistic regression model to explore the effects of rural residents’ social network and cognition on their participation in rural health governance. The research results show that only less than 30% of the respondents participated in rural health governance, and the proportion of rural resident participating is not high. Both their social network and cognition have a significant impact on their participation in rural health governance. Introverted communication helps strengthen the connection between rural residents, form the network and structure of rural social communication, build emotional links and common interests, and form a common cultural understanding paradigm and action framework. The extraverted communication means that rural residents gradually break away from the social network of acquaintances, which is not conducive to building a rural community. Rural residents’ understanding of behavior begins to deviate from rural culture, customs and emotional values, and the binding force of traditional culture is reduced, making it difficult to motivate them to participate in rural public life. Policy cognition can improve rural residents’ recognition of the value and significance of health governance. Responsibility awareness is the internal driving force for rural residents to participate in health governance, which can also reduce the governance cost of rural managers. Based on this, increasing rural residents’ introverted communication and cultivating their sense of responsibility are key to promoting their participation in rural health governance.

Coastal land use represents the combination of various land cover forms in a coastal area, which helps us understand the historical events, current conditions, and future progress of a coastal area. Currently, the emergence of high-resolution optical satellite images significantly extends the scope of coastal land cover recognition, and deep learning models provide a significant possibility of extracting high-level abstract features from an optical satellite image to characterize complicated coastal land covers. However, recognition systems for labeling are always defined differently for specific departments, organizations, and institutes. Moreover, considering the complexity of coastal land uses, it is impossible to create a benchmark dataset that fully covers all types of coastal land uses. To improve the transferability of high-level features generated by deep learning to reduce the burden of creating a massive amount of labeled data, this paper proposes an integrated framework to support semantically enriched coastal land use recognition, including foundation model-powered multi-label coastal land cover classification and conversion from coastal land cover mapping into coastal land use semantics with a vector space model (VSM). The experimental results prove that the proposed method outperformed the state-of-the-art deep learning approaches in complex coastal land use recognition.