A hybrid deep learning method for landslide susceptibility analysis with the application of InSAR data

Yuan, Rui; Chen, Jing

doi:10.1007/s11069-022-05430-8

Cited by 16 publications

(6 citation statements)

References 62 publications

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“…The collected data are displayed in real time and dynamically in the form of charts, so that it can have a better understanding of the monitored landslide situation [17]. The system can dial a pre-set phone number or forward information to a mobile phone, voice alarm, so that people can understand the development of the disaster, so that disaster can be minimized.…”

Section: Fig6 Receiver Workflowmentioning

confidence: 99%

Research on Road Geological Hazard Risk Assessment Model Based on Computational Deep Learning Algorithm Integrated With GIS

Zhichao Xu

2024

jes

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This paper studies the geological disaster emergency command and management system based on geographic information system. This system is for the province, city, county land resources and other aspects of the comprehensive application, the realization of real-time information display, meteorological early warning, group measurement and mass prevention, monitoring and early warning, major disaster management, prevention plan, quick report processing, emergency investigation, emergency disposal, emergency consultation and other functions. Through the GSM network, the monitoring data is sent to the monitoring center through the mobile phone, and then the monitoring center processes, stores, displays and alarms the monitoring data. Finally, B/S structure is used to build a remote Web data service publishing platform, so that it can carry out real-time monitoring at any place, and improve the speed, efficiency and intuition of high-speed slope disaster. It is proved that the method has the advantages of stable operation, accurate data, fast transmission speed and high monitoring accuracy through the practical application of a highway project foundation pit slope monitoring.

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Section: Fig6 Receiver Workflowmentioning

confidence: 99%

Research on Road Geological Hazard Risk Assessment Model Based on Computational Deep Learning Algorithm Integrated With GIS

Zhichao Xu

2024

jes

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“…The improvement in model performance in the previous section proves the effectiveness of CNN and TLA strategies in establishing LSP models with limited samples. However, different deep-learning models have different effects on evaluators in LSP [50]. Therefore, in order to validate the CNN framework proposed in this paper, the GRU model and the BiLSTM model are added.…”

Section: Comparison Of Lsp Modelmentioning

confidence: 99%

Transfer Learning with Attributes for Improving the Landslide Spatial Prediction Performance in Sample-Scarce Area Based on Variational Autoencoder Generative Adversarial Network

Lin

Chen

et al. 2023

Land

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Owing to the complexity of obtaining the landslide inventory data, it is a challenge to establish a landslide spatial prediction model with limited labeled samples. This paper proposed a novel strategy, namely transfer learning with attributes (TLAs), to make good use of existing landslide inventory data, a strategy that is based on a variational autoencoder of a generative adversarial network (VAEGAN) for improving the landslide spatial prediction performance in sample-scarce areas. Different from transfer learning (TL), TLAs are pretraining the model with the data reconstructed by VAEGAN, so that the models learn in advance the landslide attributes of sample-scarce areas. Accordingly, a database containing a total of 986 landslides in three study areas with 14 landslide-influencing factors was established, and each of the three models, i.e., convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM) and gated recurrent units (GRUs), was respectively selected as the feature extractor of the VAEGAN to reconstruct the data with attributes and the prediction model to generate the landslide susceptibility maps to investigate and validate the proposed TLA strategy. The experimental results showed that the TLA strategy increased the mean value of evaluators, such as the area under the receiver-operating characteristic (AUROC), F1-score, precision, recall and accuracy by about 2–7% compared with TL, results that indicated that the generated data have the attribute of specific study areas and the effectiveness of TLA strategy in sample-scare areas.

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“…At a large scale, satellite synthetic aperture radar interferometry (InSAR) enables the precise monitoring of ground deformations over tens of square kilometers. InSAR analysis thus represents one of the best instruments for creating regional-scale slope instability inventories [12,13] and relative susceptibility maps [14,15]. However, due to the line-of-sight acquisition mode of the satellite sensors, geometric distortions like shadowing effects may limit the capacity of this method to effectively investigate vertical slopes.…”

Section: Introductionmentioning

confidence: 99%

Multi-Sensor and Multi-Scale Remote Sensing Approach for Assessing Slope Instability along Transportation Corridors Using Satellites and Uncrewed Aircraft Systems

et al. 2023

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Rapid slope instabilities (i.e., rockfalls) involving highway networks in mountainous areas pose a threat to facilities, settlements and life, thus representing a challenge for asset management plans. To identify different morphological expressions of degradation processes that lead to rock mass destabilization, we combined satellite and uncrewed aircraft system (UAS)-based products over two study sites along the State Highway 133 sector near Paonia Reservoir, Colorado (USA). Along with a PS-InSAR analysis covering the 2017–2021 interval, a high-resolution dataset composed of optical, thermal and multi-spectral imagery was systematically acquired during two UAS surveys in September 2021 and June 2022. After a pre-processing step including georeferencing and orthorectification, the final products were processed through object-based multispectral classification and change detection analysis for highlighting moisture or lithological variations and for identifying areas more susceptible to deterioration and detachments at the small and micro-scale. The PS-InSAR analysis, on the other hand, provided multi-temporal information at the catchment scale and assisted in understanding the large-scale morpho-evolution of the displacements. This synergic combination offered a multiscale perspective of the superimposed imprints of denudation and mass-wasting processes occurring on the study site, leading to the detection of evidence and/or early precursors of rock collapses, and effectively supporting asset management maintenance practices.

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A hybrid deep learning method for landslide susceptibility analysis with the application of InSAR data

Cited by 16 publications

References 62 publications

Research on Road Geological Hazard Risk Assessment Model Based on Computational Deep Learning Algorithm Integrated With GIS

Research on Road Geological Hazard Risk Assessment Model Based on Computational Deep Learning Algorithm Integrated With GIS

Transfer Learning with Attributes for Improving the Landslide Spatial Prediction Performance in Sample-Scarce Area Based on Variational Autoencoder Generative Adversarial Network

Multi-Sensor and Multi-Scale Remote Sensing Approach for Assessing Slope Instability along Transportation Corridors Using Satellites and Uncrewed Aircraft Systems

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