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Intelligent Perception of Geo-Hazards from Earth Observations

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 35679

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Special Issue Editors

Prof. Dr. Qiang Xu

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State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China
Interests: landslide detection; landslide monitoring; early warning

Prof. Dr. Shunping Ji

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Guest Editor

School of Remote Sensing Information Engineering, Wuhan University, Wuhan 430079, China
Interests: machine learning and remote sensing applications

Prof. Dr. Wanchang Zhang

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Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: earth system science by remote sensing and GIS; environment remote sensing; disaster monitoring; assessment with remote sensing and GIS

Special Issue Information

Dear Colleagues,

Geo-disasters are one of the most pervasive natural hazards, which usually result in enormous human casualties and property damage. Detecting and monitoring geohazards over extensive areas with Earth observation technology by using automated methods is an urgent need, yet a challenging task in the practice of disaster prevention and mitigation at present. In recent decades, Earth observation technology in association with deep learning in artificial intelligence (AI) has drawn more and more attention, and great progress has been made particularly on new methods based on convolutional neural networks (CNNs) for geo-disaster detection, risk assessment, monitoring and early warning from optical remote sensing, InSAR, LiDAR and so on. The rapid advancement in this active field has shed light on effective and on-time responses to potential geo-disaster preventions and mitigations over disaster-prone regions.

For fostering the application of advanced machine learning and deep learning algorithms in association with Earth observations for geo-disaster prevention and mitigation, this Special Issue aims to publish works that present the use of any non-invasive technique (satellite and aerial RS, UAV, sensors installed on various equipment) in association with deep learning approaches for geo-disaster detection, susceptibility assessment and mapping, as well as early warning or long-term monitoring over extensive areas. Works devoted to the broadly understood detecting and mapping of potential geo-disasters to their retention properties are also welcomed.

Articles may address, but are not limited, to the following topics:

Rapid mapping of co-seismic landslides using earth observations (optical remote sensing, InSAR, LiDAR, etc.) in association with deep learning approaches;
Geo-disaster detection and susceptibility mapping by earth observations (optical remote sensing, InSAR, LiDAR, etc.) in association with deep learning approaches;
Potential geo-disaster detection and early warning by earth observations (optical remote sensing, InSAR, LiDAR, etc.) in association with deep learning approaches;
Long-term geo-disaster monitoring by earth observations (optical remote sensing, InSAR, LiDAR, etc.) in association with deep learning approaches.

Prof. Dr. Qiang Xu
Prof. Dr. Shunping Ji
Prof. Dr. Wanchang Zhang
Guest Editors

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Keywords

geo-disasters
intelligent perception
automatic detection
monitoring and early warning
susceptibility assessment
machine learning
deep learning
convolutional neural network

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Related Special Issue

Intelligent Perception of Geo-Hazards from Earth Observations (Second Edition) in Remote Sensing

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20 pages, 8767 KiB

Open AccessArticle

First Results on the Systematic Search of Land Surface Temperature Anomalies as Earthquakes Precursors

by Badr-Eddine Boudriki Semlali, Carlos Molina, Hyuk Park and Adriano Camps

Remote Sens. 2023, 15(4), 1110; https://doi.org/10.3390/rs15041110 - 17 Feb 2023

Cited by 8 | Viewed by 2050

Abstract

Every year, earthquakes cause thousands of casualties and high economic losses. For example, in the time frame from 1998 to 2018, the total number of casualties due to earthquakes was larger than 846 thousand people, and the recorded economic losses were about USD 661 billion. At present, there are no earthquake precursors that can be used to trigger a warning. However, some studies have analyzed land surface temperature (LST) anomalies as a potential earthquake precursor. In this study, a large database of global LST data from the Geostationary Operational Environmental Satellite (GOES) and AQUA satellites during the whole year 2020 has been used to study the LST anomalies in the areas affected by earthquakes. A total of 1350 earthquakes with a magnitude larger than M4 were analyzed. Two methods widely used in the literature have been used to detect LST anomalies in the detrended LST time series: the interquartile (IQT) method and the standard deviation (STD). To the authors’ knowledge, it is the first time that the confusion matrix (CM), the receiver operating characteristic curve (ROC), and some other figures of merit (FoM) are used to assess and optimize the performance of the methods, and to select the optimum combination that could be used as a proxy for their occurrence. A positive anomaly was found a few days before the studied earthquakes, followed by the LST decrease after the event. Further studies over larger regions and more extended periods will be needed to consolidate these encouraging results. Full article

(This article belongs to the Special Issue Intelligent Perception of Geo-Hazards from Earth Observations)

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28 pages, 7182 KiB

Open AccessArticle

Earthquake-Induced Landslide Susceptibility Assessment Using a Novel Model Based on Gradient Boosting Machine Learning and Class Balancing Methods

by Shuhao Zhang, Yawei Wang and Guang Wu

Remote Sens. 2022, 14(23), 5945; https://doi.org/10.3390/rs14235945 - 24 Nov 2022

Cited by 12 | Viewed by 2668

Abstract

Predicting the susceptibility of a specific part of a landslide (SSPL) involves predicting the likelihood that the part of the landslide (e.g., the entire landslide, the source area, or the scarp) will form in a given area. When predicting SSPL, the landslide samples are far less than the non-landslide samples. This class imbalance makes it difficult to predict the SSPL. This paper proposes an advanced artificial intelligence (AI) model based on the dice-cross entropy (DCE) loss function and XGBoost (XGB_DCE) or Light Gradient Boosting Machine (LGB_DCE) to ameliorate the class imbalance in the SSPL prediction. We select the earthquake-induced landslides from the 2018 Hokkaido earthquake as a case study to evaluate our proposed method. First, six different datasets with 24 landslide influencing factors and 10,422 samples of a specific part of the landslides are established using remote sensing and geographic information system technologies. Then, based on each of the six datasets, four landslide susceptibility algorithms (XGB, LGB, random-forest (RF) and linear discriminant analysis (LDA)) and four class balancing methods (non-balance (NB), equal-quantity sampling (EQS), inverse landslide-frequency weighting (ILW), and DCE loss) are applied to predict the SSPL. The results show that the non-balanced method underestimates landslide susceptibility, and the ILW or EQS methods overestimate the landslide susceptibility, while the DCE loss method produces more balanced results. The prediction performance of the XGB_DCE (average area under the receiver operating characteristic curve (0.970) surpasses that of RF (0.956), LGB (0.962), and LDA (0.921). Our proposed methods produce more unbiased and precise results than the existing models, and have a great potential to produce accurate general (e.g., predicting the entire landslide) and detailed (e.g., combining the prediction of the landslide source area with the landslide run-out modeling) landslide susceptibility assessments, which can be further applied to landslide hazard and risk assessments. Full article

(This article belongs to the Special Issue Intelligent Perception of Geo-Hazards from Earth Observations)

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19 pages, 5292 KiB

Open AccessArticle

A Novel Historical Landslide Detection Approach Based on LiDAR and Lightweight Attention U-Net

by Chengyong Fang, Xuanmei Fan, Hao Zhong, Luigi Lombardo, Hakan Tanyas and Xin Wang

Remote Sens. 2022, 14(17), 4357; https://doi.org/10.3390/rs14174357 - 2 Sep 2022

Cited by 23 | Viewed by 3519

Abstract

Rapid and accurate identification of landslides is an essential part of landslide hazard assessment, and in particular it is useful for land use planning, disaster prevention, and risk control. Recent alternatives to manual landslide mapping are moving in the direction of artificial intelligence—aided recognition of these surface processes. However, so far, the technological advancements have not produced robust automated mapping tools whose domain of validity holds in any area across the globe. For instance, capturing historical landslides in densely vegetated areas is still a challenge. This study proposed a deep learning method based on Light Detection and Ranging (LiDAR) data for automatic identification of historical landslides. Additionally, it tested this method in the Jiuzhaigou earthquake-hit region of Sichuan Province (China). Specifically, we generated a Red Relief Image Map (RRIM), which was obtained via high-precision airborne LiDAR data, and on the basis of this information we trained a Lightweight Attention U-Net (LAU-Net) to map a total of 1949 historical landslides. Overall, our model recognized the aforementioned landslides with high accuracy and relatively low computational costs. We compared multiple performance indexes across several deep learning routines and different data types. The results showed that the Multiple-Class based Semantic Image Segmentation (MIOU) and the F1_score of the LAU-Net and RRIM reached 82.29% and 87.45%, which represented the best performance among the methods we tested. Full article

(This article belongs to the Special Issue Intelligent Perception of Geo-Hazards from Earth Observations)

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18 pages, 10300 KiB

Open AccessArticle

Slope-Unit Scale Landslide Susceptibility Mapping Based on the Random Forest Model in Deep Valley Areas

by Hui Deng, Xiantan Wu, Wenjiang Zhang, Yansong Liu, Weile Li, Xiangyu Li, Ping Zhou and Wenhao Zhuo

Remote Sens. 2022, 14(17), 4245; https://doi.org/10.3390/rs14174245 - 28 Aug 2022

Cited by 39 | Viewed by 3260

Abstract

Landslide susceptibility evaluation is critical for landslide prevention and risk management. Based on the slope unit, this study uses the information value method- random forest (IV-RF) model to evaluate the landslide susceptibility in the deep valley area. First, based on the historical landslide data, a landslide inventory was developed by using remote sensing technology (InSAR and optical remote sensing) and field investigation methods. Twelve factors were then selected as the input data for a landslide susceptibility model. Second, slope units with different scales were obtained by the r.slopeunits method and the information value method- random forest (IV-RF) model is used to evaluate the landslide susceptibility. Finally, the spatial distribution characteristics of landslide susceptibility grade under the optimal scale are analyzed. The results showed that under the slope unit obtained when c = 0.1 and a = 3 × 10⁵ m², the internal homogeneity/external heterogeneity of 8425 slope units extracted by the r.slopeunits method is the best, with an AUC of 0.905 and an F1 of 0.908. In this case, the accuracy of landslide susceptibility evaluation is the highest as well; it is shown that the finer slope units would not always lead to the higher accuracy of landslide susceptibility evaluation results; it is necessary to comprehensively consider the internal homogeneity and external heterogeneity of the slope units. Under the optimal slope unit scale, the number of landslides in the highly and extremely highly susceptible areas in the landslide susceptibility map accounted for 82.60% of the total number of landslides, which was consistent with the actual distribution of landslides; this study shows that the method, combining the slope unit and the information value method- random forest (IV-RF) model, for landslide susceptibility evaluation can obtain high accuracy. Full article

(This article belongs to the Special Issue Intelligent Perception of Geo-Hazards from Earth Observations)

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22 pages, 3891 KiB

Open AccessArticle

Study of the Automatic Recognition of Landslides by Using InSAR Images and the Improved Mask R-CNN Model in the Eastern Tibet Plateau

by Yang Liu, Xin Yao, Zhenkui Gu, Zhenkai Zhou, Xinghong Liu, Xingming Chen and Shangfei Wei

Remote Sens. 2022, 14(14), 3362; https://doi.org/10.3390/rs14143362 - 12 Jul 2022

Cited by 25 | Viewed by 3479

Abstract

The development of landslide hazards is spatially scattered, temporally random, and poorly characterized. Given the advantages of the large spatial scale and high sensitivity of InSAR observations, InSAR is becoming one of the main techniques for active landslide identification. The difficult problem is how to quickly extract landslide information from extensive InSAR image data. Since the instance segmentation model (Mask R-CNN) in deep learning can provide highly robust target recognition, we select the landslide-prone eastern edge of the Tibetan Plateau as a specific test area. Introducing and optimizing this model achieves high-speed and accurate recognition of InSAR observations. First, the InSAR patch landslide instance segmentation dataset (SLD) is established by developing a common object in context (COCO) annotation format conversion code based on InSAR observations. The Mask R-CNN+++ is found by adding three functions of the ResNext module to increase the fineness of the network segmentation results and enhance the noise resistance of the model, the DCB (deformable convolutional block) to improve the feature extraction ability of the network for geometric morphological changes of landslide patches, and an attention mechanism to selectively enhance usefully and suppress features less valuable to the native Mask R-CNN network. The model achieves 92.94% accuracy on the test set, and the active landslide recognition speed based on this model under ordinary computer hardware conditions is 72.3 km²/s. The overall characteristics of the results of this study show that the optimized model effectively enhances the perceptibility of image morphological changes, thereby resulting in smoother recognition boundaries and further improvement of the generalization ability of segmentation detection. This result is expected to serve to identify and monitor active landslides in complex surface conditions on a large spatial scale. Moreover, active landslides of different geometric features, motion patterns, and intensities are expected to be further segmented. Full article

(This article belongs to the Special Issue Intelligent Perception of Geo-Hazards from Earth Observations)

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20 pages, 10664 KiB

Open AccessArticle

Multisource Data Fusion and Adversarial Nets for Landslide Extraction from UAV-Photogrammetry-Derived Data

by Haiqing He, Changcheng Li, Ronghao Yang, Huaien Zeng, Lin Li and Yufeng Zhu

Remote Sens. 2022, 14(13), 3059; https://doi.org/10.3390/rs14133059 - 25 Jun 2022

Cited by 6 | Viewed by 2569

Abstract

Most traditional methods have difficulty detecting landslide boundary accurately, and the existing methods based on deep learning often lead to insufficient training or overfitting due to insufficient samples. An end-to-end, semi-supervised adversarial network, which fully considers spectral and topographic features derived using unmanned aerial vehicle (UAV) photogrammetry, is proposed to extract landslides by semantic segmentation to address the abovementioned problem. In the generative network, a generator similar to pix2pix is introduced into the proposed adversarial nets to learn semantic features from UAV-photogrammetry-derived data by semi-supervised operation and a confrontational strategy to reduce the requirement of the number of labeled samples. In the discriminative network, DeepLabv3+ is improved by inserting multilevel skip connection architecture with upsampling operation to obtain the contextual information and retain the boundary information of landslides at all levels, and a topographic convolutional neural network is proposed to be inserted into the encoder to concatenate topographic features together with spectral features. Then, transfer learning with the pre-trained parameters and weights, shared with pix2pix and DeepLabv3+, is used to perform landslide extraction training and validation. In our experiments, the UAV-photogrammetry-derived data of a typical landslide located at Meilong gully in China are collected to test the proposed method. The experimental results show that our method can accurately detect the area of a landslide and achieve satisfactiory results based on several indicators including the Precision, Recall, F1 score, and mIoU, which are 13.07%, 15.65%, 16.96%, and 18.23% higher than those of the DeepLabV3+. Compared with state-of-the-art methods such as U-Net, PSPNet, and pix2pix, the proposed adversarial nets considering multidimensional information such as topographic factors can perform better and significantly improve the accuracy of landslide extraction. Full article

(This article belongs to the Special Issue Intelligent Perception of Geo-Hazards from Earth Observations)

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19 pages, 34847 KiB

Open AccessArticle

Automatic Detection of Coseismic Landslides Using a New Transformer Method

by Xiaochuan Tang, Zihan Tu, Yu Wang, Mingzhe Liu, Dongfen Li and Xuanmei Fan

Remote Sens. 2022, 14(12), 2884; https://doi.org/10.3390/rs14122884 - 16 Jun 2022

Cited by 55 | Viewed by 5158

Abstract

Earthquake-triggered landslides frequently occur in active mountain areas, which poses great threats to the safety of human lives and public infrastructures. Fast and accurate mapping of coseismic landslides is important for earthquake disaster emergency rescue and landslide risk analysis. Machine learning methods provide automatic solutions for landslide detection, which are more efficient than manual landslide mapping. Deep learning technologies are attracting increasing interest in automatic landslide detection. CNN is one of the most widely used deep learning frameworks for landslide detection. However, in practice, the performance of the existing CNN-based landslide detection models is still far from practical application. Recently, Transformer has achieved better performance in many computer vision tasks, which provides a great opportunity for improving the accuracy of landslide detection. To fill this gap, we explore whether Transformer can outperform CNNs in the landslide detection task. Specifically, we build a new dataset for identifying coseismic landslides. The Transformer-based semantic segmentation model SegFormer is employed to identify coseismic landslides. SegFormer leverages Transformer to obtain a large receptive field, which is much larger than CNN. SegFormer introduces overlapped patch embedding to capture the interaction of adjacent image patches. SegFormer also introduces a simple MLP decoder and sequence reduction to improve its efficiency. The semantic segmentation results of SegFormer are further improved by leveraging image processing operations to distinguish different landslide instances and remove invalid holes. Extensive experiments have been conducted to compare Transformer-based model SegFormer with other popular CNN-based models, including HRNet, DeepLabV3, Attention-UNet, U

^{2}

Net and FastSCNN. SegFormer improves the accuracy, mIoU, IoU and F1 score of landslide detectuin by 2.2%, 5% and 3%, respectively. SegFormer also reduces the pixel-wise classification error rate by 14%. Both quantitative evaluation and visualization results show that Transformer is capable of outperforming CNNs in landslide detection. Full article

(This article belongs to the Special Issue Intelligent Perception of Geo-Hazards from Earth Observations)

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28 pages, 22703 KiB

Open AccessArticle

Landslide Susceptibility Assessment Model Construction Using Typical Machine Learning for the Three Gorges Reservoir Area in China

by Junying Cheng, Xiaoai Dai, Zekun Wang, Jingzhong Li, Ge Qu, Weile Li, Jinxing She and Youlin Wang

Remote Sens. 2022, 14(9), 2257; https://doi.org/10.3390/rs14092257 - 7 May 2022

Cited by 26 | Viewed by 3691

Abstract

The Three Gorges Reservoir region in China is the Yangtze River Economic Zone’s natural treasure trove. Its natural environment has an important role in development. The unique and fragile ecosystem in the Yangtze River’s Three Gorges Reservoir region is prone to natural disasters, including soil erosion, landslides, debris flows, landslides, and earthquakes. Therefore, to better alleviate these threats, an accurate and comprehensive assessment of the susceptibility of this area is required. In this study, based on the collection of relevant data and existing research results, we applied machine learning models, including logistic regression (LR), the random forest model (RF), and the support vector machine (SVM) model, to analyze landslide susceptibility in the Yangtze River’s Three Gorges Reservoir region to analyze landslide events in the whole study region. The models identified five categories (i.e., topographic, geological, ecological, meteorological, and human engineering activities), with nine independent variables, influencing landslide susceptibility. The accuracy of landslide susceptibility derived from different models and raster cells was then verified by the accuracy, recall, F1-score, ROC curve, and AUC of each model. The results illustrate that the accuracy of different machine learning algorithms is ranked as SVM > RF > LR. The LR model has the lowest generalization ability. The SVM model performs well in all regions of the study area, with an AUC value of 0.9708 for the entire Three Gorges Reservoir area, indicating that the SVM model possesses a strong spatial generalization ability as well as the highest robustness and can be adapted as a real-time model for assessing regional landslide susceptibility. Full article

(This article belongs to the Special Issue Intelligent Perception of Geo-Hazards from Earth Observations)

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20 pages, 9029 KiB

Open AccessArticle

DRs-UNet: A Deep Semantic Segmentation Network for the Recognition of Active Landslides from InSAR Imagery in the Three Rivers Region of the Qinghai–Tibet Plateau

by Ximing Chen, Xin Yao, Zhenkai Zhou, Yang Liu, Chuangchuang Yao and Kaiyu Ren

Remote Sens. 2022, 14(8), 1848; https://doi.org/10.3390/rs14081848 - 12 Apr 2022

Cited by 24 | Viewed by 3432

Abstract

At present, Synthetic Aperture Radar Interferometry (InSAR) has been an important technique for active landslides recognition in the geological survey field. However, the traditional interpretation method through human–computer interaction highly relies on expert experience, which is time-consuming and subjective. To solve the problem, this study designed an end-to-end semantic segmentation network, called deep residual shrinkage U-Net (DRs-UNet), to automatically extract potential active landslides in InSAR imagery. The proposed model was inspired by the structure of U-Net and adopted a residual shrinkage building unit (RSBU) as the feature extraction block in its encoder part. The method of this study has three main advantages: (1) The RSBU in the encoder part incorporated with soft thresholding can reduce the influence of noise from InSAR images. (2) The residual connection of the RSBU makes the training of the network easier and accelerates the convergency process. (3) The feature fusion of the corresponding layers between the encoder and decoder effectively improves the classification accuracy. Two widely used networks, U-Net and SegNet, were trained under the same experiment environment to compare with the proposed method. The experiment results in the test set show that our method achieved the best performance; specifically, the F1 score is 1.48% and 4.1% higher than U-Net and SegNet, which indicates a better balance between precision and recall. Additionally, our method has the best IoU score of over 90%. Furthermore, we applied our network to a test area located in Zhongxinrong County along Jinsha River where landslides are highly evolved. The quantitative evaluation results prove that our method is effective for the automatic recognition of potential active landslide hazards from InSAR imagery. Full article

(This article belongs to the Special Issue Intelligent Perception of Geo-Hazards from Earth Observations)

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13 pages, 3371 KiB

Open AccessTechnical Note

L-Unet: A Landslide Extraction Model Using Multi-Scale Feature Fusion and Attention Mechanism

by Zhangyu Dong, Sen An, Jin Zhang, Jinqiu Yu, Jinhui Li and Daoli Xu

Remote Sens. 2022, 14(11), 2552; https://doi.org/10.3390/rs14112552 - 26 May 2022

Cited by 23 | Viewed by 3030

Abstract

At present, it is challenging to extract landslides from high-resolution remote-sensing images using deep learning. Because landslides are very complex, the accuracy of traditional extraction methods is low. To improve the efficiency and accuracy of landslide extraction, a new model is proposed based on the U-Net model to automatically extract landslides from remote-sensing images: L-Unet. The main innovations are as follows: (1) A multi-scale feature-fusion (MFF) module is added at the end of the U-Net encoding network to improve the model’s ability to extract multi-scale landslide information. (2) A residual attention network is added to the U-Net model to deepen the network and improve the model’s ability to represent landslide features. (3) The bilinear interpolation algorithm in the decoding network of the U-Net model is replaced by data-dependent upsampling (DUpsampling) to improve the quality of the feature maps. Experimental results showed that the precision, recall, MIoU and F1 values of the L-Unet model are 4.15%, 2.65%, 4.82% and 3.37% higher than that of the baseline U-Net model, respectively. It was proven that the new model can extract landslides accurately and effectively. Full article

(This article belongs to the Special Issue Intelligent Perception of Geo-Hazards from Earth Observations)

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