## 5 Conclusions  
This study proposes an efficient real-time monitoring method for urban street vegetation conditions. The research develops an innovative solution using deep learning and multimodal visual large models. We present the UGSAM, a method for real-time analysis of street view images to extract vegetation areas, which meets urban greening management requirements with high accuracy and low operational costs. The UGSAM integrates feature fusion with a multiattention mechanism Weather-Robust Processing Module, the WRPM, combined with a segmentation correction protocol network, SCP-Net. This architecture enables effective handling of image degradation caused by variable urban weather conditions while improving recognition accuracy. The correction network helps refine vegetation segmentation results by identifying overlooked vegetation areas, enhancing overall segmentation precision. We conducted comprehensive comparative experiments and ablation studies on benchmark datasets. The superior performance of the UGSAM was validated through multiple perspectives and task scenarios. In standard weather conditions, the OA of the UGSAM achieved 0.8776 for vegetation segmentation tasks, representing a 1.2% improvement over the SAM. The mIoU reached 0.8681, showing a 1.5% enhancement compared with the SAM. Under adverse weather conditions, the UGSAM demonstrated exceptional capability, with OA and mIoU surpassing the SAM by 18.9% and 25.5%, respectively, showing significant performance advantages. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Training Time (h)** & **Inferencing Time (s)** \\ \hline WRPM (FFA-Net) & 7.3 & 4.22 \\ WRPM (LS-UNet) & 5.5 & 3.68 \\ WRPM (FIM) & 3.1 & 3.07 \\ SCP-Net & 0.6 & 0.94 \\ SAM (Vit-H) & 0 (Freeze) & 56.13 \\ SAM (Vit-L) & 0 (Freeze) & 32.19 \\ SAM (Vit-B) & 0 (Freeze) & 19.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: The training and inference time of each module in the UGSAM. In the ablation experiments, we designed three sets of experiments to validate the effectiveness and superiority of each component in the UGSAM. First, we tested the performance of the UGSAM using the WRPM with fused features against using FFA-Net and LS-UNet individually. The results showed that the model with the WRPM performed the best. Second, we tested the performance of the UGSAM with and without SCP-Net, and the results demonstrated that the incorporation of SCP-Net improved the model's performance. Finally, we experimented with multiple loss function strategies during the training of SCP-Net to determine the optimal approach. The results indicated that the combination of cross-entropy loss and Dice loss yielded the best performance, which is consistent with the strategy used in the UGSAM. In conclusion, these results are of significant importance, as they are likely to be used for potentially important findings in the field of urban greening distribution recognizing. Conceptualization, C.M., H.L. and C.L. ; methodology, H.L. and C.L. ; validation, H.L. ; formal analysis, S.Y. and H.L. ; data curation, H.L. and S.Y. ; writing--original draft preparation, H.L., S.Y. and Q.Y. ; writing--review and editing, C.M. and H.L. ; visualization, J.Y. and J.F. ; project administration, C.M. and B.M. ; funding acquisition, C.M., Z.C. and F.X. All authors have read and agreed to the published version of the manuscript. This research was funded by the Science and Technology Innovation Program of Xiongan New Area (grant no. 2023 XAGG0065), the Fundamental Research Funds for the Central Universities (grant no.