Landslide mapping using a semi-Automatic Bayesian approach

Justine Douglas, Shou Hao Chiang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Landslide hazards are common in Taiwan due to its mountainous topography and high number of earthquakes and typhoons experienced yearly. As a result it is essential to develop a method of landslide detection that is capable of landslide providing results with a reasonable level of accuracy, and may also be integrated into an early warning or monitoring system. Probabilistic methods such as Bayesian analysis have gained interest in recent times over more traditional deterministic approaches due to their greater flexibility and the more informative nature of any obtained results. In this study, we will use a stepwise semi-Automatic Bayesian analysis approach for mapping landslides in Huaguoshan, Taiwan. Prior probability of landsliding will be obtained using an integrated analysis and used to detect landslides from a post-Typhoon satellite image. This information will be used to detect and map rainfall induced shallow landslides in a post typhoon environment.

Original languageEnglish
Title of host publication37th Asian Conference on Remote Sensing, ACRS 2016
PublisherAsian Association on Remote Sensing
Number of pages9
ISBN (Electronic)9781510834613
StatePublished - 2016
Event37th Asian Conference on Remote Sensing, ACRS 2016 - Colombo, Sri Lanka
Duration: 17 Oct 201621 Oct 2016

Publication series

Name37th Asian Conference on Remote Sensing, ACRS 2016


Conference37th Asian Conference on Remote Sensing, ACRS 2016
Country/TerritorySri Lanka


  • Bayesian theory
  • Integrated analysis
  • Landslide modeling
  • Taiwan


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