## 3 Methodology
### Training of the SEVnet2d Segmentation Network  
First, we assembled an aerial imagery dataset consisting of landfill and non-landfill areas. To optimize the data for input into the SEVnet model, preprocessing steps such as image cropping, resizing, and normalization were applied. Next, using the TensorFlow framework, we built the SEVnet network. The proposed Vnet2d architecture diagram is shown in Figure 3. The encoder is on the left side of the VNet network and is responsible for encoding the input ultrasound image and extracting its features. It contains four encoding modules with varying feature resolutions, each comprising different numbers of convolutional and downsampling layers. Convolutional layers extract simple and complex features from ultrasound images. To enhance feature utilization, a residual connection, similar to [29], is utilized to connect the encoder and decoder sections. Downsampling layers reduce the feature map size, which amplifies its receptive field and lowers computational complexity while decreasing parameter storage. Applying this downsampling operation improves image feature robustness by limiting attention to key information and ignoring redundancies. In a downsampling layer, to decrease memory consumption during training, a stride 2 convolution operation is used instead of traditional pooling. On the right side of the network is the decoder, which is responsible for decoding the extracted feature map and restoring the resolution of the feature map. The decoder part also contains 4 decoding modules with different feature resolutions, and each decoding module consists of different numbers of convolutional layers and upsampling layers. The architecture of the convolutional layer is similar to that of the encoder part, and the residual connection is also employed; the upsampling layer uses the deconvolution operation to restore the resolution of the feature map. After each deconvolution operation of the feature map, its size is doubled, and finally, a feature map with the same resolution as the input image can be obtained. The encoder part and the decoder part are connected through 3 convolutional layers. The employed convolutional layers all have a filler of \(5\times 5\). And the process of the convolution can be shown as follows:  
\[z_{k}(i,j)=\sum\
olimits_{n=1}^{N}\{\sum\
olimits_{p=0}^{W_{f-1}}\sum \
olimits_{q=0}^{H_{f-1}}x_{n}(i\cdot s+p,j\cdot s+q)\cdot H_{k}(p,q)\}+b_{k} \tag{4}\]  
In this equation, \(b_{k}\) is the bias parameter corresponding to the filter \(k_{S}\) is the stride and \(H_{k}\) is the weight for (\(p\),\(q\)) in the filter \(k\). The classifier of the network consists of a \(1\times 1\times 1\) convolutional layer and a sigmoid activation layer, where the \(1\times 1\times 1\) convolutional layer is used to reduce the number of feature maps, and the sigmoid activation layer is used to calculate the final feature map. The class of each pixel outputs the segmentation probability map of the network. The skip connection combines the shallow simple features in the network with the deep abstract features to obtain more refined segmentation results. We defined the training parameters, including the number of iterations, batch size, and learning rate. With our parameters set, we commenced the training process. The images, preprocessed for optimal performance, were input into the model. During this process, each image was passed through the model's architecture, initializing the training phase. Upon the completion of an epoch, the model generated predicted outputs, which were essentially segmented maps. These predictions were then compared with the actual labels. Using the Dice loss function, a commonly utilized metric in image segmentation, we calculated the divergence in the model's prediction from the true value. Next, a process called backpropagation is initiated. In this way, we determine the weights and biases that  
Figure 3: The overall methods of the proposed SEVnet.