Efficient Landslide Detection by UAV-based Multi-temporal Visual Analysis

Yosuke Yamaguchi, Kai Matsui, Jun Ohya, Katsuya Hasegawa, Hiroshi Nagahashi

研究成果: Conference article査読


This paper proposes a landslide detection method by UAV-based visual analysis. The fundamental strategy is to detect ground surface elevation changes caused by landslides. Our method consists of five steps: multi-temporal image acquisition, ground surface reconstruction, georeferencing, elevation data export, and landslide detection. In order to improve efficiency, we use Visual Simultaneous Localization and Mapping for ground surface reconstruction. It can perform faster than conventional methods based on Structure-from-Motion. In addition, we introduce convolutional neural network (CNN) to detect landslides robustly in the multi-temporal elevation data. The experimental results in a simulation environment show that the proposed method runs 5.5 times as fast as the conventional methods. In addition, the CNN-based model achieved F1 score of 0.79-0.84, showing robustness against reconstruction noise and registration error.

ASJC Scopus subject areas

  • コンピュータ グラフィックスおよびコンピュータ支援設計
  • コンピュータ サイエンスの応用
  • 人間とコンピュータの相互作用
  • ソフトウェア
  • 電子工学および電気工学
  • 原子分子物理学および光学


「Efficient Landslide Detection by UAV-based Multi-temporal Visual Analysis」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。