## 1 Introduction
In addition, the multimodal characteristics of the SAM support the fusion of multiple information, and by combining image visual features and semantic information, it can significantly improve the accuracy and stability of greening area identification, provide strong technical support for accurately distinguishing vegetation and nonvegetation areas, and significantly improve the positioning accuracy of urban greening areas [26; 27]. However, due to the complexity of urban scenes, directly applying the SAMto urban street view images may result in minor omissions in boundary regions. Therefore, enhancing the model's capability to handle boundary areas becomes essential. On the other hand, with the advent of image defogging technology, there is a possibility of utilizing these defogging technologies to address the impact of severe weather, such as heavy fog, frequently encountered in urban vegetation positioning tasks. Existing defogging methods can be categorized into traditional physical approaches and deep-learning-based techniques [28]. Traditional physical defogging methods, such as DCP [29], estimate atmospheric transmission and illumination components to achieve defogging. However, their reliance on strong assumptions about uniform fog distribution and scene brightness often leads to halo artifacts, color distortion, and difficulties in maintaining global consistency. With advancements in artificial intelligence, numerous deep-learning-based defogging methods have been developed. AOD-Net [30], leveraging a deep learning framework, simplifies the joint optimization of transmission and atmospheric light by parameterizing the atmospheric scattering model. Nevertheless, its shallow network structure limits its dynamic adaptability to varying fog concentrations, resulting in incomplete defogging. EMRA-Net [31] and GCANet [32] further incorporate multiscale residual modules, channel attention mechanisms, and gated context aggregation strategies to enhance local texture recovery and noise suppression. Despite these improvements, they still struggle with heavy fog regions, leading to incomplete processing. Defogging methods based on Transformer architectures, including Dehamer [33], DehazeFormer [34], and PMNet [35], utilize multihead self-attention mechanisms to focus on deeper image features. However, these methods tend to over-defog, causing the loss of original image characteristics. Moreover, algorithms based on the Transformer structure typically incur substantial computational costs and require extended inference times [33; 34; 35]. Hence, current defogging techniques fail to meet the requirements of urban scenarios in practice. In other words, there is a need for a defogging method that achieves high defogging quality while preserving the original features of images across different fog concentrations to the greatest extent possible. To this end, we first developed a specialized correction network, namely, the Streetscape-Correction Network (SCP-Net). SCP-Net employs an encoder-decoder architecture, where the encoder is based on ResNet and the decoder consists of multiple upsampling layers with skip connections. Additionally, it innovatively incorporates a multiloss function. This network effectively mitigates the issues of misclassification and omission of vegetation in boundary regions during image segmentation using SAM, while enabling directional correction through a dual-error correction mechanism. On the other hand, we designed the Weather-Robust Preprocessing Module (WRPM) to counteract the degradation of street view data quality caused by climatic factors. The WRPM adopts a parallel dual-branch structure for feature extraction, utilizing dense haze region processing network (FFA-Net) and light haze region processing network (LS-UNet) to, respectively, extract dense fog and light fog features from images. These branches enable dedicated processing pathways tailored to different fog concentration levels. Following feature extraction, the feature fusion module (FIM) adaptively fuses the extracted information.