Improving Landslide Recognition on UAV Data through Transfer Learning
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Landslides and related hazards · Anomaly Detection Techniques and Applications · Remote-Sensing Image Classification
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Tianjin College, University of Science and Technology Beijing, Tianjin 301830, China
Institute of Guangzhou Urban Planning Survey and Design, Guangzhou 510060, China
Department of Ocean Technology, Tianjin University of Science and Technology, Tianjin 300222, China
Funding
Tianjin Natural Science Foundation · Scientific Research Project of Tianjin Education Commission
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