## 1 Introduction
This enables a comprehensive understanding of urban environments, supporting decisions for sustainable urban development. However, even with high spatial resolution, remote sensing imagery still lags behind natural imagery in terms of resolution and quality because of the influence of factors like satellite altitude, satellite optical system performance, and manufacturing costs, resulting in the widespread existence of mixed pixels. The edge contours and surface texture information of ground objects are eroded. On the other hand, a UGS encompasses various types of plants with different spectral information, and the spectral information varies greatly between plant species. Consequently, the model needs to store a more extensive range of information to effectively capture the diversity in UGS. At the same time, high-spatial resolution remote sensing imagery contains various objects with similar spectral information, such as UGS, farmlands, forests, and some plant-rich water bodies, for which spectral distinguishment is challenging. These characteristics make the boundary of the spectrum of ground objects in the feature space more \"steep\" and thus, it is difficult to accurately extract UGS information using threshold methods or shallow learning methods. With the development of deep learning, image segmentation methods based on deep neural networks have found wide applications in tasks such as pedestrian detection [19],lane recognition [20], and object identification [21]. They have become vital solutions for urban planning, environmental monitoring, ecological research, and more. Furthermore, these methods offer new possibilities for extracting UGS from high-spatial resolution remote sensing imagery because deep learning models can easily extract complex features without manual design and substantial prior knowledge and can learn nonlinear mapping relationship between inputs and outputs [22]. Consequently, [PERSON] et al. [23] proposed a deep learning classification method for UGS, utilizing phenological characteristics as constraints. This approach takes full advantage of the spectral and spatial information provided by high-resolution remote sensing imagery from different periods. Vegetation phenological features are introduced as auxiliary bands into deep learning networks for training and classification. [PERSON] et al. [21] introduced a multi-level UGS segment architecture based on DeepLab V3+, aimed at extracting urban green space information from high-resolution remote sensing imagery. [PERSON] et al. [24] presented a general deep learning (DL) framework for large-scale urban green space mapping and generated fine-grained UGS maps (UGS-1 m) for 31 major cities in mainland China. [PERSON] et al. [25] introduced a novel hybrid approach, the Multi-Scale Feature Fusion and Transformer Network (MFFTNet), as a fresh deep learning method for extracting urban green spaces from GF-2 high-resolution remote sensing satellites. However, the task of extracting UGS is fundamentally different from natural image semantic segmentation tasks [19; 20; 21]. To begin with, UGS exhibit highly irregular and unpredictable edges compared with objects like people, cars, and buildings, resulting in irregular shapes in remote sensing imagery. Additionally, UGS span a wide range of spatial scales, encompassing large areas like parks, as well as small isolated green spaces such as individual trees and garden plots. Moreover, there are similarities in surface texture features among different UGS. Due to these factors, directly applying natural image semantic segmentation models to the task of UGS segmentation will lead to a performance decline. To address the aforementioned issues, following a thorough investigation and analysis of UGS, in this paper we proposed an end-to-end UGS segmentation model, named the Green Space Reverse Pixel Shuffle Network (GSRPnet), for extracting UGS from GF-2 remote sensing imagery.