## 4 Discussion  
### Advantages of Deep Learning Methods for Green Space Extraction  
Deep learning can automatically learn the deep features of images and has achieved remarkable results in image classification. Urban green spaces exhibit an uneven distribution and significant heterogeneity. Extraction of information regarding urban green space via remote sensing technology is susceptible to interference from other terrestrial objects, which can consequently compromise extraction accuracy. In this study, both deep learning and machine learning techniques were employed to develop a classification system (U-net-SVM). First, deep learning was used to extract the information of two categories of green space and non-green space, and second, SVM was combined to further realize the classification of irrigation and grass. The final extraction results achieved an overall classification accuracy of 94.31%. Compared with a literature study [28], the overall accuracy of directly using the U-net model for the classification of irrigation and grass in Beijing urban green space was improved by 1.23%. These findings indicate that the integration of SVM with deep learning models can enhance the classification outcomes of the precision of urban green space. Further, SVM as a shallow machine learning algorithm is limited by the computational unit and cannot provide a large dataset to solve the more complex classification problems [29]. Reclassification by using the U-net framework leads to a 10.68% increase in the accuracy of green space information derived from the SVM algorithm when compared to that from the SVM alone. This increase in accuracy serves to offset the lower classification performance of SVM regarding irrigation and grass. Furthermore, it partially addresses the inherent limitations of traditional machine learning techniques, which often struggle to accurately delineate the boundaries between various categories of urban green spaces. Consequently, this approach mitigates issues related to the misclassification and omission of certain classifications, resulting in a more comprehensive representation of the edges of the extracted green space data and enhancing the overall quality of urban green space extraction outcomes [52; 53; 54]. The U-Net reduces the influence of other feature information such as building shadows, mulch, and other features to provide more accurate green space information, with better classification results [10; 30]. These results indicate that the urban green zones extraction classification system (U-net-SVM) in Beijing exhibits a strong learning and generalization ability and can learn the feature information of green space from the GF-7 image, being highly feasible and accurate in refined urban green space RSI classification and extraction.  
The developed classification system (U-net-SVM) for urban green space extraction in Beijing, as outlined in this study, exhibited a significant degree of accuracy during experimental evaluations. However, it is crucial to acknowledge that this study exclusively considered the spectral and texture attributes of GF-7 imagery [55]. For the labeled samples, production was only limited to the Fifth Ring Road of Beijing, which may limit the generalizability of the classification system. Consequently, future studies should investigate the integration of vegetation phenology features with elevation data to enhance the scope of sample extraction. This approach aims to further augment the generalizability of the model and its capacity for precise classification in the extraction of urban forest and grassland information.