## 5 Conclusions  
This study proposes a novel semantic segmentation method for remote sensing images, named PLGTransformer, which integrates the strengths of convolutional neural networks (CNNs) and transformer architectures. It is designed to address the challenges of segmenting remote sensing imagery characterized by structural complexity and land-cover diversity. By constructing a multi-level and multi-scale feature fusion mechanism, the model effectively combines local detail extraction with global contextual understanding, thereby enhancing semantic expressiveness while maintaining boundary clarity. In the experimental section, we conducted a comprehensive evaluation on two public remote sensing datasets, ISPRS Vaihingen and Potsdam, using only RGB optical imagery. Experimental results demonstrate that PLGTransformer outperforms state-of-the-art methods across multiple mainstream evaluation metrics, exhibiting notable robustness and accuracy, particularly in complex classes such as buildings, trees, and impervious surfaces. The main contribution of this study lies in the introduction of a multi-resolution feature integration strategy that fuses shallow and deep semantics, along with the hierarchical window-based attention mechanism of the Swin transformer, which effectively models long-range dependencies. This design significantly enhances the model's ability to perceive complex spatial patterns in remote sensing imagery.  
PLGTransformer holds considerable potential for future extension. First, at the data level, modalities such as DSM, SAR, or LiDAR can be incorporated to design more sophisticated cross-modal feature fusion mechanisms, thereby improving performance in areas with complex terrain or occlusions. Second, considering similar segmentation demands in fields like medical imaging and autonomous driving, PLGTransformer demonstrates promising transferability. Future work could focus on adapting the model in terms of input modalities, architecture modifications, and inference efficiency for specific tasks. Lastly, to meet the high-resolution, large-scale, and real-time processing requirements in real-world remote sensing applications, lightweight model design, accelerated inference methods, and efficient sliding window segmentation strategies will be essential directions for further research.  
Conceptualization, Y.L. and G.W.; methodology, Y.L. and G.W.; software, Y.L.; validation, Y.L.; formal analysis, Y.L. and G.W.; investigation, Y.L. and G.W.; resources, G.W.; data curation, Y.L. and G.W.; writing--original draft preparation, Y.L.; writing--review and editing, Y.L. and G.W.; visualization, Y.L.; supervision, G.W.; project administration, Y.L. and G.W.; funding acquisition, G.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Science and Technology Development Fund, Macao SAR. Grant number: 0004/2023/ITP1.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The datasets can be freely downloaded from [[https://www.isprs.org/resources/datasets/benchmarks/UrbanSemLab/](https://www.isprs.org/resources/datasets/benchmarks/UrbanSemLab/)]([https://www.isprs.org/resources/datasets/benchmarks/UrbanSemLab/](https://www.isprs.org/resources/datasets/benchmarks/UrbanSemLab/)), accessed on 13 May 2025. The code can be made accessible by the corresponding author upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.