| dc.description.abstract | Landslide is one of the common natural hazards in the mountainous regions of Taiwan. For landslide management, it is very important to analyze the characteristics, occurrence and past-hazard events of landslides. Therefore, landslide detection is a fundamental and important task, and it is also in high demand for disaster assessment. Combining with remotely sensed data and image analysis landslide mapping task is time-effective nowadays. This study conducts landslide detection using deep learning framework with satellite remote sensing images and spatial data. Landslide recognition requires adequate data with high relationship to the occurrence of landslides. Therefore, this study manually collects past hazard events from 2000 to 2014 in Shimen and Laonong watersheds, Taiwan as training dataset. In addition, available features including optical satellite images, NDVI, gray level co-occurrence matrix (GLCM) texture features and topographic factors are adopted for detecting landslide. The former three components are bitemporal images, yet topographic factors only contain one-time stamp. Because the training data-used contains time series of two-dimensional images and multi-types input features, this study integrates feature extraction and classification processes into a two-steps training framework to adapt to the existing data for landslide identification. In the first step, ConvLSTM U-Net model is used as feature extraction model for extracting features from bitemporal images. In the second step, random forest is utilized as a final classifier to analyze input data of feature maps and topographic elements.
The trained model is employed to create landslide maps in selected ten test regions. The detected landslide maps are evaluated by quantitative indexes, e.g., Precision, Recall, F1-score, IOU and Kappa. The assessment results indicate that the developed deep learning framework can achieve the accuracies of higher than 0.7 of IOU, 0.8 of F1-score and 0.8 of Kappa. | en_US |