ABSTRACT
Landslides have been frequently occurring in the high mountainous areas in China and poses serious threats to peoples' lives and property, economic development, and national security. Detecting and monitoring quiescent or active landslides is important for predicting risks and mitigating losses. However, traditional ground survey methods, such as field investigation, GNSS, and total stations, are only suitable for field investigation at a specific site rather than identifying landslides over a large area, as they are expensive, time-consuming, and laborious. In this study, the feasibility of using SBAS-InSAR to detect landslides in the high mountainous areas along the Yunnan Myanmar border was tested first, with fifty-four IW mode Sentinel-1A ascending scenes from 12 January 2019 to 8 December 2020. Next, the Yolo deep-learning model with Gaofen-2 images captured on 5 December 2020 was tested. Finally, the two techniques were combined to achieve better performance, given each of them has intrinsic limitations on landslide detection. The experiment indicated that the combination could improve the match rate between detection results and references, which implied that the performance of landslide detection can be improved with the fusion of time series SAR images and optical images.
Subject(s)
Landslides , ChinaABSTRACT
Landslide inventories could provide fundamental data for analyzing the causative factors and deformation mechanisms of landslide events. Considering that it is still hard to detect landslides automatically from remote sensing images, endeavors have been carried out to explore the potential of DCNNs on landslide detection, and obtained better performance than shallow machine learning methods. However, there is often confusion as to which structure, layer number, and sample size are better for a project. To fill this gap, this study conducted a comparative test on typical models for landside detection in the Wenchuan earthquake area, where about 200,000 secondary landslides were available. Multiple structures and layer numbers, including VGG16, VGG19, ResNet50, ResNet101, DenseNet120, DenseNet201, UNet-, UNet+, and ResUNet were investigated with different sample numbers (100, 1000, and 10,000). Results indicate that VGG models have the highest precision (about 0.9) but the lowest recall (below 0.76); ResNet models display the lowest precision (below 0.86) and a high recall (about 0.85); DenseNet models obtain moderate precision (below 0.88) and recall (about 0.8); while UNet+ also achieves moderate precision (0.8) and recall (0.84). Generally, a larger sample set can lead to better performance for VGG, ResNet, and DenseNet, and deeper layers could improve the detection results for ResNet and DenseNet. This study provides valuable clues for designing models' type, layers, and sample set, based on tests with a large number of samples.
Subject(s)
Earthquakes , Landslides , Machine LearningABSTRACT
Conductive particles in the gas insulated transmission lines (GIL) induce the breakdown between electrodes or the flashover along insulators. To solve the problem of particle moving and realize the particle-moving regulation, particle trajectories should firstly be determined in air and SF6. This paper presents the results of particle trajectories and its charging behaviors at vacuum and SF6, respectively. Metal particles with different materials and sizes were introduced and the charge quantity was calculated. The results showed that the particle lift-off electric field in SF6 was higher than that in air under the same gas pressure, that is, the charge on particle in SF6 was about 0.789 times lower than that in air under the same condition. Besides, the lift-off electric field of particle increased with the pressure increase of SF6. The charge on particle was affected by the concentration of electric field near particle and the electrical negative features of SF6. The work provided the data support for development of DC GIL in particle defect suppression.
