Automatic Identification of Landslides Based on Deep Learning
Source abstract
Abstract
A landslide is a kind of geological disaster with high frequency, great destructiveness, and wide distribution today. The occurrence of landslide disasters bring huge losses of life and property. In disaster relief operations, timely and reliable intervention measures are very important to prevent the recurrence of landslides or secondary disasters. However, traditional landslide identification methods are mainly based on visual interpretation and on-site investigation, which are time-consuming and inefficient. They cannot meet the time requirements in disaster relief operations. Therefore, to solve this problem, developing an automatic identification method for landslides is very important. This paper proposes such a method. We combined deep learning with landslide extraction from remote sensing images, used a semantic segmentation model to complete the automatic identification process of landslides and used the evaluation indicators in the semantic segmentation task (mean IoU [mIoU], recall, and precision) to measure the performance of the model. We selected three classic semantic segmentation models (U-Net, DeepLabv3+, PSPNet), tried to use different backbone networks for them and finally arrived at the most suitable model for landslide recognition. According to the experimental results, the best recognition accuracy of PSPNet is with the classification network ResNet50 as the backbone network. The mIoU is 91.18%, which represents high accuracy; Through this experiment, we demonstrated the feasibility and effectiveness of deep learning methods in landslide identification.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Landslides and related hazards · Anomaly Detection Techniques and Applications · Flood Risk Assessment and Management
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Information Engineering, China University of Geosciences, Beijing 100083, China
School of Geophysics and Information Technology, China University of Geosciences, Beijing 100083, China
Department of Computer Science, Tangshan Normal University, Tangshan 063000, China
Development and Research Center, China Geological Survey, Beijing 100037, China
China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, China Geological Survey, Beijing 100083, China
National Institute of Natural Hazards, Ministry of Emergency Management, Beijing 100085, China
Funding
National Natural Science Foundation of China · GHFUND B of China
References (35)
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