Landslide identification using machine learning

Wang, Haojie; Zhang, Li Min; Yin, Kesheng; Luo, Hongyu; Li, Jinhui

doi:10.1016/j.gsf.2020.02.012

Cited by 184 publications

(80 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Among these influencing factors, faults, road, and stream network were represented by polylines, the Euclidean distance to the closest source was used to calculate their potential influence on landslides. The lithology is a categorical variable, and was assigned to be a dummy variable as described in the literature [66]. In this way, 14 dummy variables were used to represent 14 lithologies listed in Section 2.4.2.…”

Section: Input and Outputmentioning

confidence: 99%

See 1 more Smart Citation

Mapping Post-Earthquake Landslide Susceptibility: A U-Net Like Approach

et al. 2020

View full text Add to dashboard Cite

A serious earthquake could trigger thousands of landslides and produce some slopes more sensitive to slide in future. Landslides could threaten human’s lives and properties, and thus mapping the post-earthquake landslide susceptibility is very valuable for a rapid response to landslide disasters in terms of relief resource allocation and posterior earthquake reconstruction. Previous researchers have proposed many methods to map landslide susceptibility but seldom considered the spatial structure information of the factors that influence a slide. In this study, we first developed a U-net like model suitable for mapping post-earthquake landslide susceptibility. The post-earthquake high spatial airborne images were used for producing a landslide inventory. Pre-earthquake Landsat TM (Thematic Mapper) images and the influencing factors such as digital elevation model (DEM), slope, aspect, multi-scale topographic position index (mTPI), lithology, fault, road network, streams network, and macroseismic intensity (MI) were prepared as the input layers of the model. Application of the model to the heavy-hit area of the destructive 2008 Wenchuan earthquake resulted in a high validation accuracy (precision 0.77, recall 0.90, F1 score 0.83, and AUC 0.90). The performance of this U-net like model was also compared with those of traditional logistic regression (LR) and support vector machine (SVM) models on both the model area and independent testing area with the former being stronger than the two traditional models. The U-net like model introduced in this paper provides us the inspiration that balancing the environmental influence of a pixel itself and its surrounding pixels to perform a better landslide susceptibility mapping (LSM) task is useful and feasible when using remote sensing and GIS technology.

show abstract

Section: Input and Outputmentioning

confidence: 99%

“…Extracted from the geological map with the scale 1:200,000, and assigned to14 dummy variables as described in the literature [66].…”

Section: -29 Lithologymentioning

confidence: 99%

Mapping Post-Earthquake Landslide Susceptibility: A U-Net Like Approach

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Machine learning techniques are also getting popular in evaluating and detecting landslides [33,34]. In addition, the use of artificial intelligence (AI) technique is growing to investigate landslides, such as landslide susceptibility mapping, characterization, and prediction [35].…”

Section: Landslide Investigation: Recent Trends and Techniquesmentioning

confidence: 99%

Introductory Chapter: Importance of Investigating Landslide Hazards

Ray¹,

Lazzari²

2020

Landslides - Investigation and Monitoring

View full text Add to dashboard Cite

“…In recent years, deep learning has gained tremendous success in various remotesensing applications due to its capability of unveiling latent representations from raw data [13,31]. Wang et al [32] compared convolutional neural network (CNN) with four machine learning algorithms and the results demonstrated that CNN could achieve a better performance. However, CNN relied on its network structure, parameters settings, and training strategies, which limited its robustness [33].…”

Section: Introductionmentioning

confidence: 99%

Semi-Supervised Multi-Temporal Deep Representation Fusion Network for Landslide Mapping from Aerial Orthophotos

2021

View full text Add to dashboard Cite

Using remote sensing techniques to monitor landslides and their resultant land cover changes is fundamentally important for risk assessment and hazard prevention. Despite enormous efforts in developing intelligent landslide mapping (LM) approaches, LM remains challenging owing to high spectral heterogeneity of very-high-resolution (VHR) images and the daunting labeling efforts. To this end, a deep learning model based on semi-supervised multi-temporal deep representation fusion network, namely SMDRF-Net, is proposed for reliable and efficient LM. In comparison with previous methods, the SMDRF-Net possesses three distinct properties. 1) Unsupervised deep representation learning at the pixel- and object-level is performed by transfer learning using the Wasserstein generative adversarial network with gradient penalty to learn discriminative deep features and retain precise outlines of landslide objects in the high-level feature space. 2) Attention-based adaptive fusion of multi-temporal and multi-level deep representations is developed to exploit the spatio-temporal dependencies of deep representations and enhance the feature representation capability of the network. 3) The network is optimized using limited samples with pseudo-labels that are automatically generated based on a comprehensive uncertainty index. Experimental results from the analysis of VHR aerial orthophotos demonstrate the reliability and robustness of the proposed approach for LM in comparison with state-of-the-art methods.

show abstract

Landslide identification using machine learning

Cited by 184 publications

References 28 publications

Mapping Post-Earthquake Landslide Susceptibility: A U-Net Like Approach

Mapping Post-Earthquake Landslide Susceptibility: A U-Net Like Approach

Introductory Chapter: Importance of Investigating Landslide Hazards

Semi-Supervised Multi-Temporal Deep Representation Fusion Network for Landslide Mapping from Aerial Orthophotos

Contact Info

Product

Resources

About