Rapid urbanization and industrialization have significantly reshaped rural landscapes in metropolitan fringe areas, resulting in “hybridized” characteristics. This study establishes an analytical framework to systematically characterize hybrid rural landscapes, diagnose specific local issues, reveal their spatial differentiation patterns and driving mechanisms, and propose targeted governance strategies. Taking 124 rural units in Baoshan District, Shanghai as a case, multi-source data from the latest available years (2020–2023) were compiled as a cross-sectional snapshot, and a comprehensive indicator system integrating landscape pattern (P), social function (F), and spatial vitality (V) was developed. Utilizing multi-source geospatial data—including land-use maps, points of interest, and mobile signaling data—Gaussian Mixture Models were applied to classify typical hybrid landscape types. Spatial evolution processes and underlying driving forces were further interpreted through remote sensing imagery analysis, field investigations, and policy document reviews. Eleven distinctive hybrid rural landscape types (HTs) were characterized, forming a spatial gradient from urban to rural, encompassing “high-density urbanized” → “ecologically embedded” → “production–living integrated” → “traditional rural landscapes”. Additionally, five representative evolutionary patterns—“urban restructuring”, “ecological orientation”, “industrial-driven transition”, “transitional hybridization”, and “traditional preservation”—were identified, shaped by spatial configuration, planning policies, industrial investments, and demographic dynamics. The framework enhances understanding of the complexity and evolutionary dynamics of rural landscapes, providing theoretical insights and practical guidance for effective typological governance and targeted policy interventions.

Waterfront space is an important spatial carrier that reflects the quality of the human settlement environment. In the stage of stock-based urban renewal, value evaluation is the primary task for achieving the “precise optimization” of waterfront spaces. Based on Maslow’s hierarchy of needs, this study constructs a waterfront space value evaluation system covering basic support value, livability value, and well-being value. Taking the Suzhou Creek waterfront space in Shanghai as a case study, this research integrates data such as street-view images and Weibo check-in records, and applies deep learning, natural language processing, and other techniques to evaluate waterfront space value. Correlation analysis, principal component analysis, and spatial autocorrelation analysis are used to explore the factors associated with waterfront space value. The results show that waterfront space value presents significant spatial differences, with the eastern area near the Huangpu River showing a markedly higher value than the western area. Spatial value is associated with street environment, facility services, spatial morphology, functional experience, and spatial attractiveness. In addition, the results of spatial autocorrelation analysis indicate that optimizing functional experience and facility services in the eastern core area is of great importance, while improving online popularity and attractiveness in the western area is also important for promoting coordinated development between the eastern and western areas. The findings provide a reference for future waterfront space evaluation and offer scientific support for urban renewal and policy-making.