## 4 Experiments and Results  
### Datasets  
**DeepGlobe [3]:** The DeepGlobe dataset, known for its ultra-high resolution and challenging content, comprises 803 remote sensing images, each with a substantial pixel  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer Name** & **Output Size** & **18-Layer** & **50-Layer** \\ \hline conv1 & \(112\times 112\) & \(7\times 7\), 64, stride 2 \\ \hline conv2\_x & \(56\times 56\) & \(\begin{bmatrix}3\times 3\text{, }64\\ 3\times 3\text{, }64\end{bmatrix}\times 2\) & \(\begin{bmatrix}1\times 1\text{, }64\\ 3\times 3\text{, }64\\ 1\times 1\text{, }256\end{bmatrix}\times 3\) \\ \hline conv3\_x & \(28\times 28\) & \(\begin{bmatrix}3\times 3\text{, }128\\ 3\times 3\text{, }128\end{bmatrix}\times 2\) & \(\begin{bmatrix}1\times 1\text{, }128\\ 3\times 3\text{, }128\\ 1\times 1\text{, }512\end{bmatrix}\times 4\) \\ \hline conv4\_x & \(14\times 14\) & \(\begin{bmatrix}3\times 3\text{, }256\\ 3\times 3\text{, }256\end{bmatrix}\times 2\) & \(\begin{bmatrix}1\times 1\text{, }256\\ 3\times 3\text{, }256\\ 1\times 1\text{, }1024\end{bmatrix}\times 6\) \\ \hline conv5\_x & \(7\times 7\) & \(\begin{bmatrix}3\times 3\text{, }512\\ 3\times 3\text{, }512\end{bmatrix}\times 2\) & \(\begin{bmatrix}1\times 1\text{, }512\\ 3\times 3\text{, }512\\ 1\times 1\text{, }2048\end{bmatrix}\times 3\) \\ \hline \hline \end{tabular}
\begin{tabular}{c c c} \hline \hline
**FLOPs** & \(1.8\times 10^{9}\) & \(3.8\times 10^{9}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Architectures for ResNet18 and ResNet50 [40].  
dimension of 2448 \(\times\) 2448. This dataset has been meticulously partitioned into training, validation, and test sets, containing 455, 207, and 142 images, respectively. Such a division facilitates efficient model training and evaluation and ensures the robustness of the model's performance when generalized across varied datasets. The diversity of land cover categories covered by DeepGlobe, including urban, agricultural, pastoral, forested, fire-stricken, waste, and unclassified areas, presents a comprehensive spectrum of seven distinct classes. These categories, in conjunction with the dataset's higher resolution compared to its predecessors, offer a more complex and realistic evaluation ground for the GLSNet model, particularly in handling fine details and diverse land cover types.  
**Vaihingen [19]:** The Vaihingen dataset, with its 33 high-resolution images, each averaging 2494 \(\times\) 2064 pixels and featuring a spatial resolution of 9 cm, provides a rich dataset for urban and natural environment analysis. The inclusion of red, green, and near-infrared (NIR) channels allows for the capture of a diverse range of visual and spatial attributes, which are crucial for accurate segmentation tasks. The six distinct categories represented in the Vaihingen dataset--improvious surfaces, buildings, low vegetation, trees, cars, and background--pose a demand for the model to exhibit high precision in distinguishing common urban and natural land cover types. The high spatial resolution and channel diversity of the Vaihingen dataset serves as an ideal testbed for evaluating the GLSNet's capability to accurately segment complex scenes with intricate details.  
The characteristics of both the DeepGlobe and Vaihingen datasets, with their high resolution, diverse categories, and realistic scenarios, make them particularly suitable for evaluating the performance of GLSNet. These datasets not only challenge the model's ability to process and analyze large amounts of detailed spatial information but also verify its accuracy in segmenting various land cover types, which is essential for applications in autonomous driving, remote sensing, and urban planning.