Improving Landslide Recognition on UAV Data through Transfer Learning

Abstract

As a frequent geological disaster, landslides cause serious casualties and economic losses every year. When landslides occur, rapid access to disaster information is the premise of implementing disaster relief and reduction. Traditional satellite remote sensing may not be able to timely obtain the image data from the disaster areas due to orbital cycle and weather impacts. Visual interpretation of remote sensing data and machine learning methods need to be improved the detection efficiency. This paper studies landslide recognition based on the UAV remote sensing image. The affected area of the Zhangmu Port region in Tibet by the Nepal earthquake occurred on 25 April 2015 was selected to carry out the landslide investigation. Aiming at the problem of insufficient training sample data of landslides, we adopt the transfer learning method. The evaluation indexes show that the proposed method can automatically identify landslide disasters. Comparing with the SSD model, our new approach has better detection performance, providing thus accurate data support for scientific decision-making of disaster rescue.