Lightweight Landslide Detection Method Based On Depth Separable Convolution And Double Self-Attention Mechanism *

The landslide detection methods using remote sensing images are mostly based on the traditional convolutional neural network model with high depth and complexity. The paper proposes a lightweight method based on Depth Separable Convolution and Double Self-Attention Mechanism (DSC-DSAM) for detecting landslides in remote sensing images. This method aims to reduce storage space and improve detection speed while maintaining accuracy. In our model, it starts with using a lightweight convolutional neural network model. Then, the dual self-attention mechanism is applied to improve the accuracy. The proposed method is compared with other existing classification models, and it is shown to have advantages in memory space and detection speed while maintaining accuracy.

1. INTRODUCTION

Landslides are one of the geological disasters that occur most frequently and cause the most serious losses in China, accounting for about half of the total number of geological disasters. In addition, the hazards caused by landslides are also very serious, causing heavy casualties, serious economic and property losses. Therefore, landslide detection is of great significance to protect people’s lives and property safety.