Machine Learning Application for Coastal Area Change Detection in Gangwon Province, South Korea Using High-Resolution Satellite Imagery

  • Park, Sung Jae; 
  • Achmad, Arief Rizqiyanto; 
  • Syifa, Mutiara; 
  • Lee, Chang-Wook
Citations

WEB OF SCIENCE

13
Citations

SCOPUS

15

초록

The coastal area is one of an essential region for the ecosystem. Various kinds of effects that occur due to natural phenomena and human intervention make an impact on the change in the coastal area. In recent years, coastal erosion caused severe problems along the Korea coastline with 143 sites are experienced erosion throughout 2013. In this study, six beaches from 3 cities in Gangwon province, Republic of Korea are chosen to monitor its coastal area in 2016 and 2018. Those areas were chosen due to the high erosion severity grades according to Coastal Maintenance Project of the Ministry of Land, Infrastructure, and Transport. By using a support vector machine (SVM) classifier, high-resolution imagery from PlanetScope satellite was processed to generate the map that separate coastal area with others. The coastal area is successfully detected and calculated using SVM classifier. The result of the coastal area map is beneficial for the government or the authorities as mitigation and prevention plan.

키워드

Support vector machine; planet scope; change detection; SUPPORT VECTOR MACHINE; ENVIRONMENTS
제목
Machine Learning Application for Coastal Area Change Detection in Gangwon Province, South Korea Using High-Resolution Satellite Imagery
저자
Park, Sung Jae; Achmad, Arief Rizqiyanto; Syifa, Mutiara; Lee, Chang-Wook
DOI
10.2112/SI90-028.1
발행일
2019
유형
Article
저널명
Journal of Coastal Research
페이지
228 ~ 235