2023 Volume 63 Issue 6 Pages 277-290
There has been an increasing demand in recent years for detailed and accurate landslide maps and inventories in disaster-prone areas of subtropical and temperate zones, particularly in Asia. Most standard mapping methods require detailed fieldwork to be conducted by knowledgeable, skilled professionals. When predicting landslides, it is important to understand past landslide cases and prepare for situations in which the same phenomena occur. Developing automatic analysis methods using deep learning can contribute to the sophistication and cost of screening. This case study analyzed the potential of using deep learning convolutional neural networks（CNNs）for landslide detection with digital elevation models（DEMs）before the slide for deep-seated landslides（DLs）that occurred during Typhoon Talas. Here, we created 36,985 pieces of learning data from topographic information, which were then applied to a CNN using a multi-modal learning model. Eight types of influence factor images were created using the DEM as the learning data. The learning outcome achieved an accuracy of ＞0.856 for a 50 × 50-pixel window size CNN. This indicates that the decrease in the number of influence factor image types influenced the outcome. This study uses data from a limited range of DL sites in a topography specific to the accretionary zone. Although this CNN model is still in the initial stages of development, it accumulated many collapse cases and could contribute to disaster location screening, risk assessment, and hazard mapping during disasters.
