## 3 Results  
### Comparison of Urban Green Zone Classification Results  
The outcomes of the deep learning U-net model were evaluated and compared with those obtained from the SVM methodology. In the local extraction outcomes of the two \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Target Level** & **Standardized Level** & **Weight, W\({}_{\text{i}}\)** & **Indicator Level** & **Weight, W\({}_{\text{i}}\)** \\ \hline \multirow{6}{*}{Beijing Urban Green Space Information Comprehensive Evaluation System,} & \multirow{2}{*}{Evaluation of urban green space landscape pattern, B2} & \multirow{2}{*}{0.36} & Green coverage, C1 & 0.1188 \\  & & & Percentage of irrigation cover, C2 & 0.0621 \\  & & & Percentage of blue–green space, C3 & 0.0891 \\  & & & Landscape fragmentation, C4 & 0.1620 \\  & & & Landscape aggregation, C5 & 0.0972 \\  & & & Landscape diversity, C6 & 0.1008 \\  & & & Green belt service radius coverage, C7 & 0.2183 \\  & & & Green space accessibility, C8 & 0.1517 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Thorough assessment of the significance of each layer within the index. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Landscape Index Name** & **Unit** & **Index Meaning** & **Equation** & **Reference** \\ \hline \multirow{4}{*}{Landscape diversity} & Shannon Diversity & \multirow{2}{*}{\(--\)} & Reflects the heterogeneity and fragmentation of the landscape. & SHDI & \(=-\sum_{i=1}^{m}(p,\mu p_{i})\) \\  & Index, SHDI & & Reflects that the landscape is & \\  & Shannon Even Index, & & dominated by one or a few & \\  & SHEI & \(--\) & dominant tile types. & \\ \hline \multirow{6}{*}{Note: NP represents the aggregate quantity of landscape patches; A represents the cumulative area of landscape patches. ; E is the sum of the boundaries of various types of patches in the landscape; n represents the aggregate quantity of plaques of a specific type; \(p_{\text{i}}\) is the plaque circumference; \(a_{\text{bi}}\) is the plaque area; and \(g_{\text{ii}}\) represents the ratio of the area occupied by an i-th patch type to the overall landscape area.} \\ \  
classification techniques, the green color denotes areas of irrigation, red signifies grassland, and the uncolored regions indicate non-green spaces (Figure 4). The blue and green spaces were extracted from the water body and the green space through the formula. The classification results indicate that the performance of the U-net-SVM surpasses that of the SVM in terms of extraction efficacy (Table 5). The edges of the greenery information extracted by the U-net-SVM by using the deep learning U-net model were more complete, and they could effectively separate the green from the non-green spaces and reduce the influence of other feature information. Herein, \"green space\" refers to all types of green spaces, such as trees, shrubs, and grass. The outcomes derived solely from the SVM exhibited a pronounced distortion, commonly referred to as \"pretzelization\". The shaded regions of the image were inaccurately categorized as urban green spaces, thereby compromising the overall accuracy of the classification and recognition processes. Grassland covers a small distribution area in the city and is easily covered by tall trees and buildings, indicating omissions in the extraction of grassland by both methods. The findings derived from the confusion matrix indicate that the proposed classification system (U-net-SVM) demonstrates markedly superior accuracy compared to that of the SVM alone. Specifically, the overall classification accuracy improved by 10.68%, whereas the kappa coefficient increased by 0.17. The producer accuracy and the user accuracy of the non-green spaces were the highest, with values of 99.58% and 96.67%, respectively. The producer accuracy of the grassland was the lowest, at 57.47%, with a user accuracy of 91.34%. These findings indicate that the classification system (U-net-SVM) developed in this study for the extraction of urban green space information in Beijing could effectively retrieve such data.