## 2 Materials and Methods
### Dual Large Feature Fusion Selective Kernel Attention (DLFFSKA)  
In landslide images, the size and shape of the landslide area are usually uncertain, and there is interference from irrelevant backgrounds, resulting in inaccurate capture of the location of the landslide area by the model and an insufficient ability to represent the contour of the landslide. Therefore, we designed a DLFFSKA module based on a large selective kernel (LSK) [51]. This module constructs two feature fusion convolutions by fusing three large kernel convolutions of different sizes so that the model has a wider and adaptive contextual understanding ability, which significantly enhances the ability to distinguish between the background and the target. The network structure is shown in Figure 3.  
The DLFFSKA module utilizes depthwise convolutions with dilated properties to build a model with a broader receptive field. In this model, the input features \(X\), after passing through different convolutional kernels, can produce diverse feature representations with varying receptive fields. Let the \(i\)th depthwise convolution kernel be \(k\) with a dilation rate \(d\) and the receptive field of the output feature map be \(RF\). The relationship among these three factors is described by Equation (6):  
\[ \begin{array}{ll}RF_{1}=k_{1},&RF_{i}=d_{i}(k_{i}-1)+RF_{i-1}\\ k_{i-1}\leq k_{i},&d_{1}=1,\quad d_{i-1}<d_{i-1}\leq RF_{i-1}\end{array}  \tag{6}\]By increasing the kernel size and dilation rate, the rapid expansion of the receptive field is ensured. Compared with a larger convolutional kernel, the same receptive field is obtained by a series of convolutional decompositions, but fewer parameters are used to obtain contextual information of different ranges. Subsequently, based on this multi-scale feature information, a spatial selection mechanism is used to dynamically adjust the weights at different depths. The original LSK only used two parallel large kernel convolutions. Although the ability to capture large-scale features was enhanced, the landslide image also contains a large number of small-scale features. Therefore, the DLFFSKA model first uses a convolution with a kernel size of five (\(K=5\)) and then applies convolutions with kernel sizes of seven (\(K=7\)) and nine (\(K=9\)) on this basis to further enhance the model's focus on spatially critical background information about the target. By using a spatial selection mechanism, the model can effectively spatially select feature maps with different receptive fields. The selection of these kernel sizes is based on both theoretical and empirical considerations. Theoretically, increasing the kernel size expands the receptive field, enabling the model to capture more contextual information. However, excessively large kernels may dilute local feature details critical for delineating landslide boundaries. A balance must be struck between capturing global dependencies and preserving fine-grained spatial details. The chosen values (five, seven, and nine) ensure a hierarchical feature extraction process that enhances both large-scale structural awareness and small-scale contour accuracy. Firstly, a large kernel convolution operation is applied to the input \(X\) to obtain \(U_{i}(i=1,2,3)\), as shown in Equation (7):  
\[ \begin{array}{l}U_{1}=Conv2d(X,K=5)\\ U_{2}=Conv2d(U_{1},K=7)\\ U_{3}=Conv2d(U_{1},K=9)\end{array}  \tag{7}\]  
Secondly, a convolution with a kernel size of one is applied to \(U_{i}\), obtaining the feature representation \(U_{i}^{\prime}(i=1,2,3)\) from different large kernel convolutions as shown in Equation (8):  
Figure 3: Structure of the DLFFSKA module. The **blue rectangles** represent Conv2d layers with kernel sizes of 5, 7, and 9. The **blue cube** illustrates the convolutional prediction results of the current step in the model. The **yellow rectangles** indicate different pooling methods. The **deep pink rectangle** labeled “Concat+Conv” denotes the operation where the input first undergoes a Concat operation, followed by a Conv2d operation with a \(1\times 1\) kernel.