## 1 Introduction  
According to United Nations projections, the world's population is expected to increase by 2.25 billion, reaching a total of 9.15 billion by the year 2050 [1]. Such rapid population growth imposes significant ecological pressures on cities [2], highlighting issues such as the urban heat island (UHI) effect [3] and various pollution problems in the process of urbanization. Urban green spaces (UGS) represent a crucial component of urban ecosystems, comprising the vegetative entities within urban areas [4]. UGS play a pivotal role in the sustainable development of cities [5]. On one hand, UGS contribute significantly to enhancing the urban ecological environment [6] and addressing environmental issues like air pollution [7] and noise pollution [8]. On the other hand, UGS can improve the physical and mental well-being of residents [9], reduce stress and anxiety [10], and promote a healthier lifestyle [11]. However, the environmental pollution and land issues resulting from urban expansion and construction pose a significant threat to the urban ecosystem [12]. Therefore, for the sustainable and healthy development of cities and the advancement of the United Nations Sustainable Development Goal No. 11 [13], the rapid and accurate acquisition of UGS information has become increasingly critical. In the past decade, local government authorities conducted surveys and mapping of green spaces using field investigations. Nevertheless, these traditional survey methods were time-consuming, resource-intensive, and often provided incomplete and untimely information, and some small-scale and scattered green spaces were also neglected. This hindered the development and implementation of relevant policies aimed at sustainability. Innovative approaches for UGS survey and statistics methods are still in need of further research. With the rapid advancement of earth-observing technology, the data acquisition capability of remote sensing has significantly improved, marking the dawn of a new era in multi-platform, multi-angle, multi-sensor, all-weather, and all-time earth observation [14]. Developing a method for the accurate and rapid extraction of UGS information using multispectral or hyperspectral remote sensing imagery is a critical issue. [PERSON] et al. [15] proposed a satellite image vegetation change detection method based on the Normalized Difference Vegetation Index (NDVI), utilizing Landsat TM remote sensing data and NDVI and DEM data for multisource vegetation classification. [PERSON] et al. [16] introduced a city forest type discrimination method based on Linear Spectral Mixture Analysis (LSMA) and Support Vector Machine (SVM), which used LSMA to extract three different vegetation endmembers, including broadleaf forest, confiferous forest, and low vegetation, and their abundances. [PERSON] et al. [17] generated a fine classification system for the 2015 Global 30 m Land Cover Classification (GLC_FCS30-2015) using Landsat image time series and high-quality training data from the Global Spatial Temporal Spectra Library (GSPECLib) on the Google Earth Engine computing platform. Nevertheless, UGS are characterized by fragmentation and complex backgrounds [18]; thus, there are numerous small-scale UGS, such as roadside trees and independent trees. Small-scale UGS are frequently overlooked because the spatial resolution of multispectral or hyperspectral remote sensing imagery is often limited compared with nature imagery, and small-scale UGS cannot be found even in low-spatial resolution remote sensing imagery. The aforementioned factors will lead to a significant discrepancy between extracted UGS and ground truth. Hence, it is crucial to improve the accuracy of identifying fragmented and scattered UGS. High-spatial resolution remote sensing imagery plays a pivotal role in addressing the aforementioned challenge. Its spatial resolutions are a few meters or even less than 1 m, offering detailed texture and spectral information of ground objects.