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Extraction of road blockage information for the Jiuzhaigou earthquake based on a convolution neural network and very-high-resolution satellite images

  • Baolin Yang
  • Shixin WangEmail author
  • Yi Zhou
  • Futao WangEmail author
  • Qiao Hu
  • Ying Chang
  • Qing Zhao
Research Article
  • 19 Downloads

Abstract

Road blockage information extraction from a single-phase postdisaster image is difficult because roads are narrow and easily covered by vegetation. The traditional object-oriented image analysis method is restrictive, and its detection is slow. A deep learning algorithm, i.e., the convolution neural network (CNN), is applied to rapidly extract road blockage information. An algorithm for sample generation is designed to construct a typical sample library for CNN training, and an appropriate CNN structure and a complete detection process are designed to extract road blockage information. Finally, by taking the Jiuzhaigou earthquake on August 8, 2017, as an example, experimental verification is carried out. The kappa coefficient and the F1 score of the results are 77.60% and 87.95%, respectively. The extraction of road blockages can be completed with an efficiency of 14.59 km2 per hour. The requirements for disaster emergency monitoring can be met by the accuracy and efficiency of this method, which are better than those of the traditional object-oriented method.

Keywords

Convolution neural network Very-high-resolution satellite images Road blockage information Jiuzhaigou earthquake 

Notes

Acknowledgments

This study was supported by the National Key Research and Development Program of China (2016YFC0803000 and 2017YFB0504100) and the Youth Innovation Promotion Association of the Chinese Academy of Sciences (CAS) (2015129). The authors wish to thank Maxar Technologies Ltd. (DigitalGlobe, https://www.digitalglobe.com/), Twenty First Century Aerospace Technology Co., Ltd. (21AT, https://www.21at.com.cn) and China Centre for Resources Satellite Data and Application (CRESDA, http://www.cresda.com/CN/gywm/zxgk/index.shtml), for providing the satellite images, National Aeronautics and Space Administration (NASA, http://reverb.echo.nasa.gov/reverb/) for providing the new global DEM (GDEM V2) data, and Environmental Systems Research Institute Inc. (ESRI, 380 New York Street, Redlands, CA 92373, USA) for providing the OSM data. The authors are grateful to the anonymous reviewers and editors for their valuable comments and suggestions that improved this manuscript.

Author contributions

Shixin Wang and Futao Wang conceived and designed the experiments, and Baolin Yang performed the experiments and wrote the paper. Yi Zhou provided crucial guidance and support throughout the study. Qiao Hu, Ying Chang and Qing Zhao analyzed the data.

Compliance with ethical standards

Conflict of interest

The authors declare no conflicts of interest.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

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