## 4 Results
### Comparison Experiment of the GS2018 Dataset  
In this section, we describe experiments conducted on the GS2018 dataset. As shown in Table 4, compared with other models, GSRPnet achieved the best Pre, F1-score, IoU, and OA, with values of 89.328%, 89.427%, 81.344%, and 92.256%, respectively. Overall, when compared with the worst-performing Segnet, our model exhibited remarkable improvements in Pre (5.069%), F1-score (4.207%), IoU (6.576%), and OA (3.188%). Moreover, compared with the second-best-performing SGCNnet, GSRPnet still demonstrated improve  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Description** \\ \hline U-net [27] & \begin{tabular}{c} Unet’s distinctive U-shaped structure incorporates contracting and \\ expansive pathways, enabling the model to capture contextual information. \\ \end{tabular} \\ \hline PSPnet [28] & \begin{tabular}{c} PSPnet utilizes a pyramid pooling module to capture contextual \\ information at multiple scales. \\ \end{tabular} \\ \hline Segnet [29] & \begin{tabular}{c} Segnet uses an encoder–decoder structure with a skip architecture to \\ capture and reconstruct spatial information effectively. \\ \end{tabular} \\ \hline DeepLab V3+ [30] & \
\begin{tabular}{c} DeepLab V3+ incorporates atrous convolution [39] and a feature pyramid \\ to capture fine details and context in images. \\ \end{tabular} \\ \hline SGCNnet [31] &
\begin{tabular}{c} SGCNNet applies graph convolutional networks to segmentation tasks to \\ capture contextual information of channel and spatial features. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 3: A comparison of the models used in this paper.  
ments of 1.306% in Pre, 0.431% in the F1-score, 0.725% in IoU, and 0.4% in OA. Particularly noteworthy is the minimal cost of increased model parameters for GSRPnet compared with the U-net model, with a slight increase of 3.21 M. Simultaneously, considering GSRPnet's model parameters compared with the other four comparison models, it has great advantages, even falling below one-third of DeepLab V3+. These results quantitatively reflect the effectiveness of GSRPnet in UGS segmentation tasks and demonstrate that the improved performance of the GSRPnet model is achieved not only through an increase in parameters.  
Figure 11 illustrates the visualized UGS segmentation results of a typical UGS across all comparison models. These areas include urban outskirts, squares, parks with associated water bodies, areas around railways, airport surroundings, and transportation hubs. Because of the special encoder-decoder and skip-connected structure, the U-net model retains more low-rank features; thus, the UGS edges of the recognition results are smoother. However, in the case of a scattered UGS, the recognition accuracy of U-net is reduced. PSPnet provides relatively accurate segmentation of the UGS, but errors in recognition occur when small non-UGS patches appear within the UGS. SegNet and DeepLab V3+ exhibit a jagged pattern in regions where the UGS and non-UGS intersect each other. In particular, the two models do not perform well when UGS are in a strip-like pattern. Overall, DeepLab V3+ outperforms Segnet. SGCNnet produces UGS segmentation results with smooth edges, which aligns better with ground truth. However, it does not perform well in areas where a UGS and non-UGS are in contact. The proposed GSRPnet in this paper demonstrates a smoother edge performance compared with the other models, accurately identifying discrete small UGS, especially in areas where UGS and non-UGS are interspersed with each other, and the output results closely resemble the ground truth.