## 2 Preliminaries  
### SE Block  
Starting from the relationship between feature channels, [PERSON]'s team proposed squeeze-and-excitation networks (SENets) [26; 27] to explicitly model the interdependence between feature channels, which is achieved through feature recalibration. In other words, the importance of each feature channel is automatically obtained by learning, and then useful features are promoted according to this importance, while features that are not useful for the current task are suppressed. Figure 2 is a schematic diagram of the SE module. Given an input \(x\), the number of feature channels is \(c_{1}\), and after a series of general transformations such as convolution, a feature with the number of feature channels \(c_{2}\) is obtained. Unlike in traditional CNNs, the previously obtained features need to be recalibrated next.  
The first is the squeeze operation, which performs feature compression according to the spatial dimension, and turns each two-dimensional feature channel into a real number. This real number has a global receptive field to some extent, and the output dimension matches the number of input feature channels. It characterizes the global distribution of responses across feature channels and enables layers close to the input to obtain a global receptive field, which is useful in many tasks. This operation generates channel statistics through global average pooling so that global spatial information is compressed into channel descriptors. Formally, the statistic \(z\) is obtained by shrinking \(u\) in the spatial dimension \(H\), where the \(c\)-th element in \(z\) can be calculated as follows:  
\[z_{c}=F_{sq}(u_{c})=\frac{1}{H\times W}\sum_{i=1}^{H}\sum_{j=1}^{W}u_{c}(i,j) \tag{1}\]  
Figure 1: Framework of the landfill site design based on image processing and ecological restoration theory.  
Figure 2: Diagram of the SE module. SENet uses a new neural network through automatic learning to obtain the importance of each channel in the feature map and then uses this importance to assign a weight value to each feature so that the neural network can focus on certain feature channels.  
The transformation output, \(u\), can be regarded as a collection of local descriptors, and its statistics can be used to express the whole image. This information is often utilized in feature engineering works [23; 24]. This operation successfully solves the problem that the learning filter only has a local receptive field and cannot utilize other contextual information outside this area. The second is the excitation operation, which is a mechanism similar to gates in recurrent neural networks. Weights are generated for each feature channel by a parameter \(W\), which is learned to explicitly model the correlation between feature channels. Finally, there is a reweight operation, which regards the weight of the output of excitation as the importance of each feature channel after feature selection, and then weights the previous features by channel by channel multiplication to complete the original feature in the channel dimension recalibration operation. SE modules can be embedded in almost all network structures at present. Different kinds of SENets can be obtained by embedding SE modules in the building block units of the original network structure.