The accurate and timely detection of landslides is of great significance for reducing the threats to human life and properties, along with relevant losses, caused by landslides. This study proposed a landslide detection method using feature fusion based on convolutional neural networks (CNNs) and Segmentation Transformer (SETR). The CNN-based models utilized a fully convolutional network (FCN), U-Net, and Deeplabv3+, while the Transformer-based models used SETR. First, the landslide detection effects of the CNN-based models were evaluated. Then, SETR was introduced into the encoders of the CNN-based models, and the output of SETR was fused into the CNN decoder structure as the final output of the models. The experiments using the LandSlide4Sense dataset indicate that the fusion of typical CNNs with SETR can effectively improve the landslide detection effects. After SETR fusion, the FCN, U-Net, and Deeplabv3+ models exhibited higher F1-scores, which increased from 0.672 6, 0.727 3, and 0.687 3 to 0.686 9, 0.743 0, and 0.705 5, respectively. Given the close relationship between landslides and terrain, a digital elevation model (DEM) was incorporated into the U-Net model, which outperformed other models. As a result, the F1-score of the model increased from 0.732 5 to 0.750 3.
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