# A Novel Hybrid Method for Urban Green Space Segmentation from High-Resolution Remote Sensing Images
## 1 Introduction  
UGS is one of the important components of the urban ecosystem and plays an important role in ecological environment, public health, social economy, and other aspects [1, 2, 3, 4]. In recent years, to promote sustainable planetary health, providing balanced UGS resources for urban residents has become an increasingly important goal for governments and institutions at all levels around the world [5, 6]. To promote equitable access to UGS, it is essential to gain insights into the distribution of UGS. Such insights can aid in the development of well-informed policies and the allocation of funds [7]. While the Statistical Yearbookmay provide an approximate area of UGS in a given region or city, obtaining precise UGS distribution data remains a challenge [8]. Furthermore, some areas lack reliable information regarding UGS distribution, posing significant obstacles to effective policy development and resource allocation. Therefore, to supply reliable basic geographical information for in-depth UGS research, it is crucial to conduct fine-grained and accurate extraction of UGS. With the advancement and application of remote sensing technology, a variety of remote sensing images have become valuable sources of data for obtaining urban geographical information coverage [9]. For instance, [PERSON] et al. [10] utilized MODIS data to extract UGS in certain Chinese cities. [PERSON] et al. [11] constructed GlobeLand30 data based on Landsat images. In another study, [PERSON] et al. [12] calculated object UGS coverage based on Landsat images to evaluate changes in health benefits when UGS exists. However, despite the rich data provided by multispectral images, their lower spatial resolution often limits the precision of the UGS information obtained. Furthermore, compared with low- and medium-resolution remote sensing images such as MODIS and Landsat, HRS images can provide more and more detailed ground information, helping to refine the extraction of UGS. At the same time, UGS extraction methods mainly include traditional machine learning methods and DL methods. Machine learning includes maximum likelihood methods, Random Forest, support vector machines, etc. For example, [PERSON] et al. [13] and [PERSON] et al. [14] applied machine learning methods to extract green space coverage information. However, these methods necessitate manual feature engineering for classification, which can be time-consuming, labor-intensive, and have a low degree of automation. DL can automatically extract multi-level features and finds widespread application across domains [15], including computer vision [16], natural language processing [17], and more. In the realm of remote sensing image interpretation, DL algorithms such as U-Net [18], FCNs [19], SegNet [20], DeepLabv3+ [21], etc., have been widely adopted. For example, [PERSON] et al. [22] used DeepLabv3+ and [PERSON] et al. [23] used residual networks to automatically obtain UGS distribution from GF-2 images, respectively. The automated extraction of high-resolution UGS has assumed increasing importance [24; 25]. Although deep learning methods have achieved good application results in UGS classification tasks, current methods mostly focus on the improvement of DL networks, ignoring the spectral information of HRS images. Harnessing this spectral information to its full potential has the potential to further improve the accuracy of UGS classification. To this end, we propose a MAFANet network that incorporates feature engineering. The network uses ResNet50 as the backbone network. In terms of feature extraction, we constructed a multiscale pooling attention (MSPA) module. The MSPA module focuses on extracting the contextual information of multiscale UGS, which in turn enhances the relevance of the MAFANet model in capturing long-range feature information of UGS, which is more effective than ordinary convolution in extracting features. The decoder module consists of a new decoder block (DE) and a bilateral feature fusion (BFF).