AUTOMATIC DETECTION OF SLOPE FAILURE REGIONS USING SEMANTIC SEGMENTATION

• KANAI Kazuki

  愛媛大学大学院理工学研究科 博士前期課程

• YAMANE Tatsuro

  東京大学大学院新領域創成科学研究科 博士後期課程

• ISHIGURO Satoshi

  愛媛大学 法文学部

• CHUN Pang-jo

  東京大学大学院 工学系研究科

Bibliographic Information

Other Title

• Semantic Segmentationを用いた斜面崩壊領域の自動検出

Abstract

In Japan, slope failures associated with earthquakes and heavy rainfall occur frequently. In order to assess the damage, several organizations, including the Geographical Survey Institute (GSI), have drawn maps showing the slope failure area from aerial photographs. However, in the mapping process, the workers are manually reading the slope failure area visually, which requires a lot of labor and costs. In addition, it is difficult to map the area manually, which hinders the rapid assessment of the damage. To solve this problem, research is being conducted to detect slope failure area using artificial intelligence technology such as deep learning. In this study, we propose a method for automatic detection of slope failure regions using semantic segmentation by deep learning. The establishment of this method is aimed at efficient detection of slope failure areas and rapid assessment of damage.

Journal

• Intelligence, Informatics and Infrastructure

  Intelligence, Informatics and Infrastructure 1 (J1), 421-428, 2020-11-11

  Japan Society of Civil Engineers

Keywords

• semantic segmentation
• deep learning
• mask r-cnn
• slope failure

Details

• CRID: 1390849376476759168
• NII Article ID: 130007940747
• DOI: 10.11532/jsceiii.1.j1_421
• ISSN: 24359262
• Text Lang: ja
• Data Source:
  • JaLC
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed