IED-GCN: An Internal and External Decoupled Graph Convolutional Network for Landslide Susceptibility Assessment

Landslides are one of the most frequent and destructive geological disasters, often causing significant threats to human life and infrastructure. Landslide susceptibility assessment (LSA) plays a vital role in disaster prevention and risk management. Graph convolutional networks (GCNs) have demonstrated strong potential in LSA due to their ability to model graph-structured data. However, existing GCN-based models often suffer from feature aggregation issues, where the neighborhood aggregation of landslide conditioning factors (LCFs) leads to information loss and reduced predictive performance. To address this limitation, we propose an internal and external decoupled GCN (IED-GCN) based on superpixel segmentation. First, the simple linear iterative clustering (SLIC) algorithm is employed to segment the study area into superpixels, creating more meaningful spatial units. Then, the proposed model applies internal graph convolution (IGC) to capture local feature dependencies and external graph convolution (EGC) to model global feature relationships. Furthermore, edge decoupling is incorporated into the EGC to mitigate overfitting and enhance the model’s generalization capability. The effectiveness of IDE-GCN is validated using a dataset comprising historical landslide occurrences and 13 LCFs in the Three Gorges Reservoir area. The dataset is divided into 70% of the data for training and 30% for testing. Experimental results demonstrate that our IED-GCN significantly outperforms conventional models such as convolutional neural networks (CNN), residual convolutional networks (ResNet), dense convolutional networks (DenseNet), and miniGCN, achieving F1 score, Kappa coefficient, and AUC values of 0.9839, 0.9675, 0.9838. In summary, our IED-GCN effectively addresses the challenge of feature aggregation in LSA tasks, offering superior performance in geological disaster prediction with complex spatial data.