In contrast to conventional machine learning techniques, deep learning methodologies can more comprehensively analyze the spectral, textural, and potential feature information inherent in extensive datasets while also facilitating automatic feature selection and extraction, which has led to the high accuracy of deep learning methods in image classification and recognition [14]. Recently, deep learning has emerged as a significantly important methodological approach for the extraction of feature information. Deep learning-based methodologies for remote sensing image classification have been employed in the extraction of building features [15; 16], road extraction [17], land classification [18; 19], and crop categorization [20], and significant progress has also been made in the area of urban green spaces [13; 20]. In the field of urban green space classification via remote sensing images, [PERSON] et al. [18] used the FCN-8s model to extract street-view green and blue spaces from the Tencent map and street-view data, and further determined the relationship between green and blue space of street-view images and depression in the elderly. [PERSON] and [PERSON] considered that irrespective of the method used, the precision of green space classification in high-spatial-resolution images was the highest, and the deep learning method showed superior performance compared to traditional methods [19]. [PERSON] et al. used various CNN architectures (Ostu, Meanshift, FNEA, and two Deeplabv3+variants), and classified five tree species in tropical urban environments by using aerial photographs with an accuracy of 86% [20]. Currently, deep learning and machine learning methods still bring forward some problems in the classification research on urban green space. For example, in the Evaluation Standard for Urban Landscaping (GB/T50563-2010), the traditional per capita green space area, green coverage rate, urban green space rate, and other indicators exhibit difficulties in describing the layout of urban park green space at a spatial resolution. There are few correlation analyses of the basic characteristics of green space and the landscape pattern index of all parks in a region, and the scope of previous studies is small. It is difficult to distinguish small patches or complex plant communities. With the rapid renewal of the city, numerous small green spaces with blurred boundaries and inaccurate information occur inside a park, which impedes the acquisition and research on green space information. As an important index of park environmental quality, blue and green space and the landscape pattern index can provide a reference for the construction of urban green space and living environments. Against this background, numerous studies on urban parks have emerged [21; 22; 23; 24]. Previous studies mostly used satellite images such as TM/ETM + as a data source [25] and focused on the evaluation of green landscape quality and ecological service value using a single index, such as blue-green space and landscape patterns of single or several parks [26; 27; 28; 29; 30]. In the new round of the Beijing Master Plan, Beijing is positioned as a \"Livable City\", which shows the great importance attached to the urban ecological environment and the urgent demand for green space [31]. To promote the \"new urbanization with people as the core\" and \"comprehensive improvement in the quality of cities\", and to adapt to the new requirements of China's economic and social development on urban landscaping [32; 33; 34; 35; 36], this study tried to expand some of the evaluation indices of the Evaluation Standard for Urban Landscaping (GB/T 50563-2010) and lay the foundation for the construction of a new evaluation system for urban landscaping [34; 35; 36; 37]. Consequently, to investigate the application prospects of high-resolution remote sensing imagery, the establishment of a comprehensive evaluation system for the landscape pattern of urban gardening and greening initiatives in Beijing as it enters a new phase of development [38; 39; 40; 41].