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Semantic Segmentation Algorithm of Landslide Based on Remote Sensing Image and DEM

The fast landslide segmentation algorithm based on deep learning technology for remote sensing images can play an important role in disaster analysis and risk assessment. At this stage, deep learning-based landslide segmentation algorithms mainly use RGB images as training data for the models, and a few studies have started to add digital elevation models (DEMs) as training data. In order to study the effect of DEM as training data on the accuracy improvement of landslide segmentation algorithm, we use RGB remote sensing images and DEM as training data, use U-Net combined with pspnet network structure, add FCN network structure in the training process, and analyze the test results using various evaluation metrics. Finally, we conclude that compared with the model using only RGB images as input data, adding DEM as input data can effectively improve the overall accuracy of the landslide segmentation model and reduce the false accept rate of the model in complex terrain.