A practical trial of landslide detection from single-temporal Landsat8 images using contour-based proposals and random forest: a case study of national Nepal

Landslides are frequent all around the world, causing tremendous loss to human beings. Rapid access to the locations where landslides occur is crucial for emergency response. Most researches in landslide detection from remotely sensed images focus on small regions, which are handpicked. That makes it easy to distinguish landslides from background objects, but hard to apply in practical cases. The complicated non-landslide background pixels increase the difficulty to accurately detect landslides. In this study, we propose a technique framework to remove non-landslide background pixels for national Nepal using 12 Landsat8 images and digital elevation model (DEM). DEM is useful in removing flat areas, where landslides are less likely to occur. The framework consists of three sections: image enhancement, landslide proposal extraction, and detection model setup. Bare land, including landslides, is enhanced using vegetation index after haze/cloud re-movement. Later, calculate connective contours and propose them as potential regions that may contain landslides. For each proposal, calculate texture feature and build detection model using one of the Landsat8 images, which is further applied on other images to check its applicability and robustness. The assessment shows that the method is able to remove 99% of the background pixels in the scale of national Nepal, taking over billions of pixels. Even there is still much to do to achieve high accurate landslide detection results from large-scale images, the experiment validates a strong potential applicability for the proposed method in large-scale landslide-related analysis.