The extraction of evergreen vegetation in urban areas holds significant importance for environmental monitoring and sustainable urban development. To address the limitations of existing visible light vegetation indices in environmental adaptability and the critical role of sample annotation in vegetation segmentation, this paper proposes a sample optimization method that integrates color theory and EfficientSAM, aiming to enhance the accuracy of evergreen vegetation extraction in urban areas. This method utilizes high-resolution remote sensing images from the visible light spectrum, combining the color sensitivity of visible light vegetation indices with the prompting capabilities of EfficientSAM to optimize samples. The optimized results are then used to train semantic segmentation models, effectively achieving precise extraction of evergreen vegetation. Experimental results demonstrate that the proposed method achieves improvements of 83.83%, 92.23%, 89.72%, and 90.96% in m_I, m_P, m_R, and m_F metrics, respectively, compared to traditional manually annotated sample training results. Furthermore, the method effectively distinguishes vegetation in water bodies from evergreen vegetation, providing a valuable reference for the accurate extraction of evergreen vegetation using visible-band imagery.

Urban attractiveness is an important indicator for measuring the comprehensive competitiveness of cities and the sustainable development of a region. Taking the city cluster in the middle reaches of the Yangtze River as the study area, the study constructs an evaluation model of urban attractiveness based on amenities theory, and uses spatial and temporal statistical tools such as trend analysis to reveal the spatial and temporal evolution trends and distribution patterns of urban attractiveness in the study area between 2011 and 2022. The results show that: (1) the level of attractiveness of the city cluster in the middle reaches of the Yangtze River has increased significantly during the study period, especially in the period of 2015 to 2019, which is the fastest rate of increase. The effectiveness of regional cooperative development is obvious, but the local differences between core cities and peripheral cities are still obvious, and the inter-cluster effect has not yet been formed. (2) The city cluster has entered the stage of multi-center synergistic development, with core cities and emerging cities jointly promoting the attractiveness, and cities such as Xiangtan, Yiyang, and Jiujiang showing greater growth potential. (3) The radiation-driven role of Wuhan has not yet been fully realized, and it is necessary to focus on strengthening Wuhan's radiation effectiveness to narrow the regional gap in the future.