## 1 Introduction  
With socio-economic development, population growth, and accelerated industrialization, most cities continue to expand. Urban expansion serves as the foundation for urban development; yet, during this process, substantial urban green spaces and suburban vegetation are frequently occupied and transformed into residential buildings and industrial facilities [1]. Urban greenery plays a crucial role in achieving urban carbon neutrality and improving air quality [2; 3; 4]. The reduction in green spaces has led to multiple environmental challenges, including intensified urban heat island effects and diminished urban ecosystem diversity [5; 6]. Therefore, comprehensive planning of urban green spaces during urban development becomes an inevitable choice for environmental preservation [7; 8; 9; 10]. Previous studies have proposed various methods for coordinated planning of urban green spaces, all requiring precise localization of vegetation within urban areas. Traditional manual field surveys demand on-site personnel inspections, proving time-consuming and cost-prohibitive for large-scale urban applications [11; 12]. Advances in remote sensing technology enable automated all-weather collection of streetscape data, allowing researchers to locate urban green spaces through street view image analysis. Progress in artificial intelligence (AI) has led to automated localization methods using machine learning and deep learning algorithms, which employ automatically acquired streetscape data combined with image segmentation algorithms like FCN [13] and PointNet++ [14] to demarcate vegetation areas [15]. However, there are two challenges in using these artificial intelligence algorithms [16; 17; 18]. First, there are limitations in the performance of these algorithms, which cannot handle the boundary regions well during segmentation, and there is a lot of room for improvement in segmentation accuracy [19]. Secondly, the urban climate is highly variable, and in some coastal cities there is often inclement weather such as storms, and most of the areas of the streetscape images collected under adverse weather are blurred and the data quality is poor, which leads to a low accuracy of the final localization [20]. Furthermore, in order for these AI algorithms to function effectively in practice, it is imperative to consider their substantial computational costs and inference times [21]. Therefore, providing urban managers with a method suitable for vegetation positioning in urban scenarios is of practical significance. Fortunately, on the one hand, multimodal large language models have emerged as a dominant approach in various artificial intelligence applications and are widely adopted in image segmentation and object detection research. The Segment Anything Model (SAM) is a multimodal large language model designed for image segmentation [22]. The SAM has demonstrated the high efficiency in performing diverse segmentation tasks through training on extensive datasets, and its pretraining model architecture and parameters make it able to quickly adapt to the greening recognition task of different scenes without complex parameter tuning [23]. Consequently, this suggests that there is potential for resolving the issues associated with substantial computing resources and inference time when implementing the SAM in practice. Meanwhile, when applied to urban vegetation localization, the SAM effectively addresses the performance limitations of traditional algorithms. For example, compared with traditional image segmentation algorithms, the SAM performs better in dealing with complex backgrounds, occlusions, and morphological diversity of vegetation in street view images, and can effectively reduce segmentation errors caused by image complexity [24; 25].