## 2 Methods
### EfficientNetV2 Model  
With the increasing research on neural networks in recent years, scholars have been paying attention to the speed of the network's training and inference. Most of the previous high-precision classification algorithms were difficult to use in embedded devices and other endpoints because of the large model size. In April 2021, the convolutional neural network framework EfficientNetV2, published in CVPR, addressed the issue that most classification algorithms improve accuracy only by expanding a single dimension of the depth, width, and resolution of the neural network [31]. EfficientNetV2 employs a balanced scaling approach across these three dimensions, allowing the model to be optimized for different hardware and requirements, thereby achieving the effect of increased precision. The EfficientNetV2 model can be trained in a shorter time than the most advanced models. Gradually increasing the image size during training can further expedite this process, although it may lead to a decrease in accuracy. To address the issue of accuracy loss, [PERSON] et al. [32] introduced a progressive learning strategy that dynamically adjusts regularization, such as data augmentation, as well as image size. The performance of EfficientNetV2 on mobile devices has also been improved, making it more suitable for deployment in resource-constrained environments [31]. In the experiment, we utilized the EfficientNetV2-S model was employed with a reduced input image size to conserve computer memory and reduce training time. The model structure is illustrated in Figure 2. The overall architecture of the model comprises eight modules. Module 1 serves as the Stem layer, incorporating a convolutional layer with a 3 \(\times\) 3 kernel size and a stride of 2, the SiLU activation function layer, and batch normalization (BN) layer for initial feature extraction from the image. Modules 2 to 4 consist of Fused-MBConv. Modules 5 to 7 comprise MBConv convolutional structures. Both of them utilize 3 \(\times\) 3 convolutional units and identify deep image features through incremental scaling of the image size using expansion factors. Lastly, the Head module consists of a convolutional layer with a 1 \(\times\) 1 kernel size. Following dimensionality reduction via convolution, an image classifier is obtained through pooling and fully connected layers. The different depth levels of convolutional layers within the network structure enable feature extraction at various levels when processing image data. Low-level convolutions aid in distinguishing basic feature types in urban land remote sensing images such as roads, buildings, and water bodies. Middle-level convolutions can extract complex features like geometric structures and local patterns that assist in distinguishing feature classes with similar textures and colors, such as public spaces and squares. High-level convolutions capture abstract features to enhance classification accuracy for remote sensing images. Figure 2: The structure of EfficientNetV2-s, which contains eight blocks. The MBConv and Fused-MBConv modules serve as the core components of the model structure. The MBConv convolution consists of a \(1\times 1\) normal convolution, a \(3\times 3\) depth-separable convolution with SiLU activation function layers and bulk normalization (BN) layers, a Squeeze-and-Excitation (SE) module, and a Dropout layer. In cases where the expansion factor in the model is equal to one, the first upscaled \(1\times 1\) convolutional layer is omitted. The final output features of the model include the Dropout layer and shortcut branch (Shortcut connection) weights. The Fused-MBConv module closely resembles the overall MBConv convolution but replaces the \(1\times 1\) upscaled convolution and the \(3\times 3\) depth-separable convolution in the main branch of the original MBConv structure with a normal \(3\times 3\) convolution while eliminating the SE module.