Landslide Mapping Using Two Main Deep-Learning Convolution Neural Network Streams Combined by the Dempster-Shafer Model

Abstract

Beyond the direct hazards of earthquakes, the deposited mass of earthquake-induced landslide (EQIL) in the riverbeds causes the river to thrust upward. The EQIL inventories are generated mostly by the traditional or semisupervised mapping approaches, which required a parameter's tuning or binary threshold decision in the practical application. In this study, we investigated the impact of optical data from the PlanetScope sensor and topographic factors from the ALOS sensor on EQIL mapping using a deep-learning convolution neural network (CNN). Thus, six training datasets were prepared and used to evaluate the performance of the CNN model using only optical data and using these data along with each and all topographic factors across the west coast of the Trishuli river in Nepal. For the first time, the Dempster-Shafer (D-S) model was applied for combining the resulting maps from each CNN stream that trained with different datasets. Finally, seven different resulting maps were compared against a detailed and accurate inventory of landslide polygons by a mean intersection-over-union (mIOU). Our results confirm that using the training dataset of the spectral information along with the topographic factor of the slope is helpful to distinguish the landslide bodies from other similar features, such as barren lands, and consequently increases the mapping accuracy. The improvement of the mIOU was a range from approximately zero to more than 17%. Moreover, the D-S model can be considered as an optimizer method to combine the results from different scenarios.