Abstract

Landslide is one of the most dangerous disasters, especially for countries with large mountainous terrain. It causes a great damage to lives, infrastructure and environments, such as traffic congestion and high accidents. Therefore, automated landslide detection is an important task for warning and reducing its consequences such as blocked traffic or traffic accidents. For instance, people approaching the disaster area can adjust their routes to avoid blocked roads, or dangerous traffic signs can be positioned in time to warn the traffic participants to avoid the interrupted road ahead. This paper proposes a method to detect blocked roads caused by landslide by utilizing images captured from Unmanned Aerial Vehicles (UAV). The proposed method comprises of three components: road segmentation, blocked road candidate extraction, and blocked road classification, which is leveraged by a multi-stage convolutional neural network model. Our experiments demonstrate that the proposed method can surpass over several state-of-the art methods on our self-collected dataset of 400 images captured with an UAV.