## 4 Experiments and Results
### Experimental Results
#### 4.4.2 Results and Analysis on the DeepGlobe Dataset  
In our thorough evaluation on the DeepGlobe dataset, our proposed GLSNet was quantitatively compared against several state-of-the-art approaches, including TransUnet [44], Mask2 Former [23], FCN-88 [45], DeepLabv3+ [7], SegNet [11], PSPNet [10], ICNet [12], UNet [8], and GLNet [20]. The comparison was not limited to segmentation accuracy (mIoU) but also encompassed the measurement of GPU memory usage (Memory).  
As depicted in Table 3, all methods demonstrated improved mIoU results with the incorporation of a global branch, as opposed to relying solely on local patches. However, this enhancement came at the cost of a significant increase in GPU memory consumption. The majority of the methods struggled to effectively balance the segmentation accuracy with GPU memory usage. Among the listed approaches, only GLNet, which employs global-local information sharing, achieved a higher mIoU with reduced memory consumption, thus being selected as the benchmark model for this dataset.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Method** & **Impervious Surface** & **Building** & **Low Vegetation** & **Tree** & **Car** & **OA** & **Mean F1** & **mIoU** \\ \hline FCN-8s [45] & 90.0 & 93.0 & 77.7 & 86.5 & 80.4 & 88.3 & 85.5 & 75.5 \\ UNet [8] & 90.5 & 93.3 & 79.6 & 87.5 & 76.4 & 89.2 & 85.5 & 75.5 \\ SegNet [11] & 90.2 & 93.7 & 78.5 & 85.8 & 83.9 & 88.5 & 86.4 & 76.8 \\ EncNet [14] & 91.2 & 94.1 & 79.2 & 86.9 & 83.7 & 89.4 & 87.0 & 77.8 \\ RefineNet [13] & 91.1 & 94.1 & 79.8 & 87.2 & 82.3 & 88.9 & 86.9 & 77.1 \\ CCEM [46] & 91.5 & 93.8 & 79.4 & 87.3 & 83.5 & 89.6 & 87.1 & 78.0 \\ DeepLabv3 [6] & 91.4 & 94.7 & 79.6 & 87.6 & 85.8 & 88.9 & 87.8 & 79.0 \\ S-RA-FCN [45] & 90.5 & 93.8 & 79.6 & 87.5 & 82.6 & 89.2 & 86.8 & 77.3 \\ PSPNet [10] & 90.6 & 94.3 & 79.0 & 87.0 & 70.7 & 89.1 & 84.3 & 74.1 \\ TransUNet [44] & 92.2 & 93.9 & 83.7 & **88.3** & 87.4 & 89.3 & 89.1 & 80.4 \\ Mask2 Former [23] & 91.4 & 94.2 & 82.0 & 86.4 & 86.0 & 88.3 & 88 & 78.1 \\ BSNet [9] & 92.1 & 94.4 & 83.1 & **88.3** & 86.7 & 90.3 & 88.9 & 80.2 \\ \hline GLSNet & **94.4** & **95.1** & **83.4** & 87.6 & **90.4** & **90.9** & **90.2** & **81.4** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison results of various approaches on the Vaihingen dataset.  
In contrast to the baseline model GLNet, the proposed GLSNet achieved significant breakthroughs in both mIoU and GPU memory usage. The mIoU score reached 72.4%, marking a 0.8% improvement over the baseline model. Most notably, the GPU memory usage was substantially decreased by 451 MB, a reduction of 24.1%. These advancements position GLSNet as a more advantageous model in terms of operational speed and resource utilization, offering enhanced potential for practical applications. We conducted an in-depth analysis to elucidate the performance differences between GLSNet and the baseline GLNet. The innovative dual-branch structure and the enhanced transformer attention mechanism of GLSNet allow for more efficient feature extraction and fusion, leading to superior segmentation accuracy while maintaining low memory usage. Specifically, the optimized architecture of GLSNet minimizes redundant computations and capitalizes on the strengths of both global and local information, resulting in a notable increase in segmentation performance.  
Furthermore, we have considered the practical implications of our findings, recognizing that the balanced approach of GLSNet to accuracy and efficiency could be beneficial for real-world applications, especially in environments with constrained computational resources. The performance of GLSNet suggests that it may offer a viable solution for 3D object detection tasks that require a balance between precision and resource utilization. We believe that the results achieved by GLSNet contribute valuable insights for the ongoing research and may support the development of more effective 3D object detection systems in the future.