Presentation information

Oral presentation

General Session » [General Session] 13. AI Application

[1D1] [General Session] 13. AI Application

Tue. Jun 5, 2018 1:20 PM - 3:00 PM Room D (4F Cattleya)

座長:肥田 剛典(東京大学)

1:40 PM - 2:00 PM

[1D1-02] Landslide Detection Using CNN with SAR Images before/after the Disaster and DEM

〇Daisuke Ueda1, Shingo Mabu1, Takashi Kuremoto1 (1. Yamaguchi University )

Keywords:Landslide Detection, Convolutional Neural Network, Satellite image

Remote sensing using synthetic aperture radar (SAR) images has attracted attention as a method of disaster area detection. However, there is a problem that a lot of experts and time are required for wide-area SAR image interpretation. Therefore, in this research, we propose a method that automatically detects landslide disaster areas using convolutional neural network (CNN). The proposed method uses not only the SAR images after disaster occurs, but also the images before the disaster and altitude data (DEM). In the experiments, the accuracy of classification as disaster area and non-disaster area in the testing areas was 75.56%, and intersection over union (IoU) was 21.86% that showed the ratio of the areas classified as disaster to the actual disaster areas. From these results, it was clarified that the landslide disaster areas could be classified by CNN considering the features of SAR images and DEM data before and after the disaster.