## 3 Results
### Comparative Experiments
#### 3.2.2 Green Space Segmentation Accuracy  
We compared the accuracy of K-means, FCN, PointNet++, the SAM, and the UGSAM in segmenting green regions in images under normal weather conditions. Through this performance evaluation, we aimed to demonstrate the superiority of the UGSAM in the field of green region segmentation. Table 5 provides a detailed comparison of the performance metrics of these models for green region segmentation in streetscape data under normal weather conditions. The UGSAM achieved an OA of 0.8776, which represents a 7.15% improvement over PointNet++ and a 1.03% improvement over the SAM. Its mIoU reached 0.8681, showing an 8.25% improvement over PointNet++ and a 1.25% improvement over the SAM. The recall score of 0.9634 indicates that the model excels at detecting vegetation regions to the greatest extent. Additionally, the UGSAM achieved a precision score of 0.8977, surpassing all other models. The final results demonstrate that the UGSAM significantly improves segmentation \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **OA** & **Recall** & **Precision** & **mIoU** \\ \hline K-means & 0.6667 & 0.7761 & 0.7429 & 0.6118 \\ FCN & 0.7755 & 0.8846 & 0.8415 & 0.7582 \\ PointNet++ & 0.8061 & 0.9091 & 0.8537 & 0.7865 \\ SAM & 0.8673 & 0.9506 & 0.8953 & 0.8556 \\ UGSAM & **0.8776** & **0.9634** & **0.8977** & **0.8681** \\ \hline \hline \end{tabular} The best results are highlighted in bold.  
\end{table} 
Table 5: Experimental results comparing the UGSAM with conventional methods in normal condition. The checkpoint of the SAM is ViT-H.  
accuracy compared to K-means, FCN, PointNet++, and the SAM, highlighting its superior performance in green region segmentation tasks. Figure 8 shows an example of the original data input. We spatially matched the acquired street view data and ultimately formed a 360-degree panoramic input image. Figure 9 shows the streetscape data completed by UGSAM segmentation. As can be seen from the images, the UGSAM demonstrates powerful performance, with extremely high accuracy in segmenting objects. After completing the segmentation, the mask decoder of the UGSAM segments the specified regions based on the prompt information encoded in the prompt encoder. Figure 10 displays the final segmented vegetation areas. Figure 8: The 360\({}^{\circ}\) panoramic streetscape imagery after haze removal. Figure 10: Extracted streetscape images with vegetation areas separated. Figure 9: The panoramic image after segmentation using the UGSAM in normal weather conditions. Meanwhile, under adverse urban weather conditions, we compared the accuracy of K-means, FCN, PointNet++, the SAM, and the UGSAM in segmenting green regions in images. Through this performance evaluation, we aimed to demonstrate the capability of the UGSAM to handle diverse and challenging urban climate conditions. Table 6 provides a detailed comparison of the performance metrics of these models for green region segmentation in streetscape data under adverse weather conditions. The UGSAM achieved an OA of 0.8615, an mIoU of 0.8490, a recall of 0.9345, and a precision of 0.9027, outperforming all other models. The accuracy of other models significantly decreases compared to their performance under normal weather conditions. The final results indicate that, compared to K-means, FCN, PointNet++, and the SAM, the UGSAM delivers the most outstanding performance in adverse weather scenarios, further validating its robustness and effectiveness in challenging environments. Figure 11 demonstrates the segmentation results of images under adverse weather conditions, both with and without the WRFM module. As can be observed from the figure, the original images exhibit a certain degree of haze, resulting in reduced clarity. In the segmentation results of the images without WRFM processing, some trees are not identified, and multiple trees are incorrectly grouped into a single object. Additionally, the distinction between trees and buildings is not clearly defined.