A Comparation of CNN and DenseNet for Landslide Detection

Landslide is a widely developed disaster in the world, which brings serious harm to economy and people. How to detection landslide intelligently, quickly and accurately has become the focus and difficulty of researchers in the field of geological hazards. In recent years, with the development of remote sensing technology and computer technology, researchers have built various landslide detection automatic or semi-automatic models. Compared with traditional interpretation methods and machine learning methods, various landslide detection models based on deep learning algorithm such as convolutional neural network (CNN) perform better and more intelligent. DenseN et is an improved CNN algorithm based on image classification, which was proposed in 2017. In this paper, we transferred the DenseNet and CNN to construct landslide detection models. After accuracy comparison, it can be shown that DenseNet performs better than CNN. The Kappa coefficient and F1 score is of 0.965, 0.995 for DenseNet, and 0.908, 0.896 for CNN, respectively. The results showed that the DenseN et model has a higher landslide detection accuracy and generalization ability.