Landslide Recognition Based on CNN and Transformer Feature Fusion

Landslide disasters are highly destructive and harmful, seriously threatening the safety of human life and property. Timely and accurate identification is crucial for disaster reduction and relief. Currently, Convolutional Neural Network (CNN) has good ability to extract local features of images, and Transformer can capture long-distance dependencies when processing sequence data. However, in the method that combines CNN and Transformer, the feature information of both cannot be fully utilized. Therefore, this article proposes an improved MLTransUNet method, which combines the information obtained by the convolution and Transformer modules through multi-level feature fusion. This method was applied to the Zigui to Badong section of the Three Gorges Reservoir area in Hubei Province. Experimental results showed that MLTransUNet achieved F1 score, Kappa and MIoU of 0.8560, 0.8499, and 0.8680, respectively. Compared with the TransUNet model, it improved by 2.62%, 2.75%, and 2.08%, respectively. This indicates that it has better performance in landslide recognition and provides technical support for landslide disaster identification and management.