A METHOD FOR AUTOMATIC DETECTION OF LANDSLIDE AREAS FROM AERIAL PHOTOGRAPHS USING MASK R-CNN

Abstract

In Japan, there have been many landslide disasters caused by earthquakes and heavy rains. The Geospatial Information Authority of Japan (GSI) prepares maps of collapsed areas to assess the damage, but it takes a lot of time for technical experts to decipher the collapsed areas manually. In recent years, there has been research on the detection of landslides using machine learning with deep learning. However, the research is still in its early stages, and the methods and analysis data still need to be accumulated. In this study, we propose a method for detecting landslides using Semantic Segmentation based on deep learning, and compare the results of detection with different training data in order to improve the efficiency of locating landslides. In addition, this study aims to detect landslides using only post-disaster aerial photographs to efficiently understand the damage situation, which is also highly novel.