## 6 Conclusions  
GLSNet, a segmentation model optimized for ultra-high resolution images prioritizing memory efficiency, has been presented. It creates a network structure composed of a shallow branch that covers the global context and a deep branch that focuses on local details, ensuring the effective collection of both global and local information. The innovative GL-Deep Fusion seamlessly combines global contextual information and local intricacies, bringing about a transformative effect. GLSNet showcases its competitive performance on both the DeepGlobe and Vaihingen datasets using this method. Specifically, it excels at producing exceptional outcomes in the process of separating overlapping small objects within an image. We consider it essential to strike an improved balance between the utilization of GPU memory and the accuracy of segmentation when exploring ultra-high resolution image research. Therefore, the GLSNet network proved to be a key solution. It is a powerful tool for solving the problem of ultra-high resolution image segmentation. Although GLSNet has already shown efficient memory usage, there remains untapped potential for additional optimization. In our upcoming research, we aim to further investigate various prospects related to ultra-high-resolution image segmentation. We intend to expand the range of uses for GLSNet to incorporate a greater variety of real-life situations. Simultaneously, we will explore the fusion of multi-modal data, including the integration of ultra-high resolution images with LiDAR, radar, or hyperspectral imagery, aiming to improve both segmentation accuracy and contextual comprehension. These endeavors will contribute to further enhancing the performance and applicability of ultra-high resolution image segmentation technology. The authors confirm their contribution to the paper as follows: study conception and design: [PERSON], [PERSON] and J.M. ; data collection: C.L. ; analysis and interpretation of results: C.L. ; draft manuscript preparation: [PERSON], K.H. and J.M. All authors have read and agreed to the published version of the manuscript. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline
**Method** & **Core Contribution** \\ \hline FCN (Series) [31; 45] & Introduced fully convolutional networks for semantic segmentation. \\ \hline U-Net (Series) [8; 25; 32; 33] & Utilizes an encoder–decoder architecture with skip connections to integrate features from various levels, particularly effective for medical image segmentation. \\ \hline DeepLab (Series) [4; 5; 6; 7] & Utilizes atrous convolution and pyramid pooling modules to effectively expand the receptive field and capture multi-scale contextual information. \\ \hline GLNet [20] & a dual-branch network that leverages multi-level feature pyramid networks (FPNs) to exchange features between branches, improving feature utilization. \\ \hline MaskFormer (Series) [22; 23] & Introduces Transformer decoders and proposes a mask classification model that unifies semantic, instance, and panoptic segmentation tasks by predicting a set of binary masks. \\ \hline Our Method & Utilizes a global shallow branch and a local deep branch in conjunction with GL-Deep Fusion based on branch-attention, achieving complete collaboration between the dual-branch structure, which is highly suitable for the field of ultra-high resolution image segmentation. \\ \hline \end{tabular}
\end{table} 
Table 7: The main contributions of those already reported in the state of the art. **Funding:** [PERSON] reports financial support was provided by the Natural Science Foundation (3502Z202372018) of Xiamen, China, and the Department of Education (JAT232012) of the Fujian Province of China. [PERSON] reports financial support was provided by the Natural Science Foundation (2021J01858) of Fujian Province of China and the Xiamen Science and Technology Subsidy Project (2023 CXY0318). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable.