A Multi-modal Deep Neural Network Model for Forested Landslide Detection

Objectives Vegetation widely spread in the southwestern mountainous regions of China. In the remote sensing images of this area, the landslides are usually shaded by vegetation. The error rate of forested landslide detection in remote sensing images is high, which is hard to meet practical needs.

Methods To address this issue, this paper uses light detection and ranging (LiDAR)-derived digital elevation mode (DEM) and hillshade to remove the forest on the landslides. In addition, a new dataset for forested landslide detection is also constructed. On this basis, an intelligent landslide detection model base on multi-modal deep learning is proposed. The proposed model uses DEM and hillshade to identify forested landslides, which consists of three neural network models：A transformer network for automatically extracting DEM features, a transformer network for automatically extracting hillshade features, and a convolution neural network with attention mechanism for merging multi-modal remote sensing data.

Results The proposed model is compared with ResU-Net, LandsNet, HRNet and SeaFormer. Experimental results show that the proposed model achieves the highest prediction accuracy. Intersection over union and F1 are improved by 9.3% and 6.8%, respectively.

Conclusions LiDAR is able to remove the impact of forest cover, which is suitable for identifying the forested landslides in the southwest mountain areas of China. The proposed LiDAR-based landslide detection model is able to predict the position of landslides, which is useful for deciding the position of landslide monitoring devices.