Landslide Detection Using Densely Connected Convolutional Networks and Environmental Conditions

A complete and accurate landslide map is necessary for landslide susceptibility and risk assessment. Currently, deep learning faces the dilemma of insufficient application, scarce samples, and poor efficiency in landslide recognition. This article utilizes the advantages of dense convolutional networks (DenseNets) and their modified technique to solve the three proposed problems. For this purpose, we created a new landslide sample library. On the original remote sensing image, 12 geological, topographic, hydrological and land cover factors that can directly or indirectly reflect the landslide are superimposed. Then, landslide detection was carried out in the three Gorges reservoir area in China to test the performance of the improved method. The quantitative evaluation of the landslide detection map shows that the combination of environmental factors and DenseNet can improve the accuracy of the detection model. Compared with the optical image, kappa and F1 increased by 9.7% and 9.1% respectively. Compared with other traditional neural networks and machine learning algorithms, DenseNet has the highest kappa and F1 values. Based on the base Densenet, through data augmentation and fine-tuning optimization technology, the kappa and F1 values reach the highest values of 0.9474 and 0.9505, respectively. The proposed method has promising applicability in large area landslide identification scenarios.

Automated summary

This study addresses the issues of insufficient accuracy, scarcity of samples, and low efficiency in landslide mapping by utilizing DenseNets and a new sample library. By incorporating environmental factors and optimizing DenseNet, the accuracy of detecting landslides improves significantly, showing promising applicability in large-scale landslide identification scenarios.