Landslide Detection Using Satellite and Aerial Imagery: Machine Learning Approach for Early Warning Systems

Landslides are dangerous phenomena that threaten infrastructure, people, and the environment, so their detection has to be swift and accurate. Conventional methods of detecting landslides are based on the visual analysis of the area, which is both time-consuming and inaccurate. In this paper, we design a deep learning model for detecting landslides from satellite and aerial images. Our approach is to preprocess the image data, extract features, and then classify them with a CNN. More specifically, for the accurate segmentation of the landslide-prone regions, we employ a CNN-based U-Net architecture, and for the dimensionality reduction and improvement of the RGB imagery, we use Principal Component Analysis (PCA). The model proposed in this paper has been trained on a dataset consisting of images of areas that are prone to landslides and those that are not. The experiments show that the method proposed in this paper is effective in detecting landslides in different areas. The application of deep learning in landslide detection can be useful in enhancing the existing early warning systems and disaster management.