## 2 Materials and Methods
### Green Space Classification and Extraction System (U-net-SVM)
#### 2.3.1 Information Extraction on Urban Green Spaces  
(1) Deep learning model construction  
In this study, a deep learning model was employed to assess the comprehensive coverage of green spaces within the study area. U-Net models usually use a variety of  
Figure 2: Flowchart of the method.  
indicators in the evaluation, which can be used to comprehensively assess the model performance. In particular, in the image segmentation task, IoU is an important evaluation index that represents the ratio between the intersection of the model prediction result and the real label; the larger the IoU, the better the segmentation effect of the model. In this study, the U-Net architecture, which demonstrates consistent classification performance, was selected as the deep learning classifier [42; 43]. It refers to a fully convolutional neural network that operates in an end-to-end manner, is distinguished by the exclusive use of convolutional layers, devoid of any fully connected layers, and can handle input images of different sizes. The encoder uses a common convolutional neural network structure containing a convolutional layer and a maximum pooling layer for down-sampling. Shallow down-sampling captures some simple information about the image, such as boundaries and colors; as the sensory field becomes larger and undergoes more convolutional operations, the deeper structure captures the abstract features of the image.  
Production of a training sample set for Beijing urban green space extraction  
Notably, green plants have high reflectivity in the near-infrared band; therefore, the spectral information of the GF-7 remote sensing data can be used as the classification feature to continuously label green and non-green spaces to construct training samples. By simulating standard false-color images, RGB was synthesized for display in the near-infrared, red, and green light bands. By integrating field survey with high-resolution remote sensing imagery, the outcomes of visual interpretation could be refined, leading to the generation of a more precise labeled image [5; 13; 18; 38; 44; 45].  
Deep learning algorithms are difficult to be trained directly on high-resolution large-format images and labeled images. Therefore, in this study, a regular grid cropping method was adopted to crop the images and corresponding labeled images of the two test sample areas with image blocks measuring 256 \(\times\) 256 in size (see Figure 3). Cropping must ensure that the numbering of the small images obtained by cropping is consistent with that of the corresponding labeled images; in total, 2450 images and corresponding labeled images were obtained by cropping. Subsequently, the green space sample dataset was augmented through horizontal, vertical, and diagonal flipping. After processing, the number of training and validation sets was expanded to 7350.  
Figure 3: Schematic illustration of regular grid clipping for RS imagery and label image: (**a**) RS imagery of the test areal (the red areas indicate vegetation with the highest reflectance in the near-infrared band); (**b**) corresponding truth label data (the green areas indicate the true vegetation after annotation).  
(3) Deep Learning Model Training and Prediction  
By using the urban green space extraction training sample dataset, the preliminary urban green space extraction model was developed through the training of the U-Net model. During the training process, network parameters, including batch size and learning rate, were systematically modified to increase the classification accuracy. The quantity of categories was established at 2, with a total of 20,000 iterations, a batch size of 10, and a fundamental learning rate of 1 \(\times\) 10\({}^{-3}\). After several training iterations, the validation data were enhanced, the number was expanded to 3 times the original, and the training model was completed.