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Remote Sensing
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Advancements in Remote Sensing and Artificial Intelligence for Geohazards
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Advancements in Remote Sensing and Artificial Intelligence for Geohazards

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Earth Observation for Emergency Management".

Deadline for manuscript submissions: closed (31 October 2024) | Viewed by 5501

Special Issue Editors

Prof. Dr. Xianmin Wang

E-Mail Website
Guest Editor
School of Geophysics and Geomatics, China University of Geosciences (Wuhan), Wuhan 430074, China
Interests: landslide; floods; artificial intelligence; remote sensing
Dr. Omid Ghorbanzadeh

E-Mail Website
Guest Editor
Institute of Advanced Research in Artificial Intelligence (IARAI), Vienna, Austria
Interests: artificial intelligence; natural hazards; remote sensing; GIS
Special Issues, Collections and Topics in MDPI journals
Prof. Domenico Calcaterra

E-Mail Website
Guest Editor
Department of Earth, Environment and Resources Science, University of Naples Federico II, 80126 Naples, Italy
Interests: landslides; floods; sinkholes; remote sensing; risk analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Geohazards frequently cause significant human and economic losses worldwide, presenting persistent challenges to social and urban sustainability. It is of great urgency to accurately, timely, and effectively evaluate, prevent, and control geohazards and to clearly understand their evolutionary characteristics, trends, and rules. With the rapid development of remote sensing (RS), along with the success of artificial intelligence (AI), they have increasingly become vital technologies for geohazard perception, cognition, prediction, and so on. Correspondingly, we have also witnessed more effective prevention and controlling of various types of geohazards, as well as significant decreases in social and economic losses from such phenomena. It is necessary to deeply explore and greatly facilitate the application of remote sensing in AI in geohazards so that we may overcome the difficulties caused by geohazards.

This Special Issue aims to gather studies covering the applications of and advancements in RS and AI for all types of geohazards. Multisource RS data, state-of-the-art AI algorithms, and manifold geohazard prevention services, among other issues, are welcome.

Article types include Articles, Reviews, Case Reports, Data Descriptors, and Technical Notes. Articles may address, but are not limited to, the following topics:

  • Geohazard monitoring and detection;
  • Geohazard risk assessment and vulnerability;
  • Geohazard prediction and spatial modeling;
  • Geohazard evaluation;
  • Geohazard emergency rescue;
  • Geohazard simulation;
  • Geohazard evolution;
  • Disaster chain;
  • Multiple hazards;
  • Geohazard datasets.

Prof. Dr. Xianmin Wang
Dr. Omid Ghorbanzadeh
Prof. Domenico Calcaterra
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • remote sensing
  • artificial intelligence
  • geohazards
  • deep/machine learning
  • evolutionary computation
  • slope failure
  • geohazard chain
  • floods
  • dam breach
  • surface collapse

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

20 pages, 2618 KiB  
Article
Enhanced Tailings Dam Beach Line Indicator Observation and Stability Numerical Analysis: An Approach Integrating UAV Photogrammetry and CNNs
by Kun Wang, Zheng Zhang, Xiuzhi Yang, Di Wang, Liyi Zhu and Shuai Yuan
Remote Sens. 2024, 16(17), 3264; https://doi.org/10.3390/rs16173264 - 3 Sep 2024
Viewed by 749
Abstract
Tailings ponds are recognized as significant sources of potential man-made debris flow and major environmental disasters. Recent frequent tailings dam failures and growing trends in fine tailings outputs underscore the critical need for innovative monitoring and safety management techniques. Here, we propose an [...] Read more.
Tailings ponds are recognized as significant sources of potential man-made debris flow and major environmental disasters. Recent frequent tailings dam failures and growing trends in fine tailings outputs underscore the critical need for innovative monitoring and safety management techniques. Here, we propose an approach that integrates UAV photogrammetry with convolutional neural networks (CNNs) to extract beach line indicators (BLIs) and conduct enhanced dam safety evaluations. The significance of real 3D geometry construction in numerical analysis is investigated. The results demonstrate that the optimized You Only Look At CoefficienTs (YOLACT) model outperforms in recognizing the beach boundary line, achieving a mean Intersection over Union (mIoU) of 72.63% and a mean Pixel Accuracy (mPA) of 76.2%. This approach shows promise for future integration with autonomously charging UAVs, enabling comprehensive coverage and automated monitoring of BLIs. Additionally, the anti-slide and seepage stability evaluations are impacted by the geometry shape and water condition configuration. The proposed approach provides more conservative seepage calculations, suggesting that simplified 2D modeling may underestimate tailings dam stability, potentially affecting dam designs and regulatory decisions. Multiple numerical methods are suggested for cross-validation. This approach is crucial for balancing safety regulations with economic feasibility, helping to prevent excessive and unsustainable burdens on enterprises and advancing towards the goal of zero harm to people and the environment in tailings management. Full article
(This article belongs to the Special Issue Advancements in Remote Sensing and Artificial Intelligence for Geohazards)
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20 pages, 4253 KiB  
Article
Integrating Satellite Images and Machine Learning for Flood Prediction and Susceptibility Mapping for the Case of Amibara, Awash Basin, Ethiopia
by Gizachew Kabite Wedajo, Tsegaye Demisis Lemma, Tesfaye Fufa and Paolo Gamba
Remote Sens. 2024, 16(12), 2163; https://doi.org/10.3390/rs16122163 - 14 Jun 2024
Cited by 1 | Viewed by 1684
Abstract
Flood is one of the most destructive natural hazards affecting the environment and the socioeconomic system of the world. The effects are higher in the developing countries due to their higher vulnerability to disaster and limited coping capacity. The Awash basin is one [...] Read more.
Flood is one of the most destructive natural hazards affecting the environment and the socioeconomic system of the world. The effects are higher in the developing countries due to their higher vulnerability to disaster and limited coping capacity. The Awash basin is one of the flood-prone basins in Ethiopia where the frequency and severity of flooding has been increasing. Amibara district is one of the flood-affected areas in the Awash basin. To minimize the effects of flooding, reliable and up-to-date information on flooding is highly required. However, flood monitoring and forecasting systems are lacking in most basins of Ethiopia including the Awash basin. Therefore, this study aimed to (i) identify important flood causative factors, (ii) evaluate the performance of random forest (RF), linear regression, support vector machine (SVM), and long short-term memory (LSTM) machine learning models for flood prediction and susceptibility mapping in the Amibara area. For developing flood prediction and susceptibility modeling, nine causative factors were considered, namely elevation, slope, aspect, curvature, topographic wetness index, soil texture, rainfall, land use/land cover, and curve number. The Pearson correlation coefficient and information gain ratio (InGR) techniques were used to evaluate the relative importance of the factors. The machine learning models were trained and tested using 400 historic flood points collected from the 10 September 2020 Sentinel 2 image, during which a flood event occurred in the area. Multiple metrics, namely precession, recall, F1-score, accuracy, and receiver operating characteristics (area under curve), were used to evaluate the performance of the models. The results showed that all the factors considered in this study were important; elevation, rainfall, topographic wetness index, aspect, and slope were more important while land use/land cover, curve number, curvature, and soil texture were less important. Furthermore, the results showed that random forest outperformed in predicting and mapping flooding for the study area whereas the linear regression model showed the next best performance to RF. However, SVM performed poorly in flood prediction and susceptibility mapping. The integration of satellite and field datasets coupled with state-of-the-art-machine learning models are novel approaches and thus improved the accuracy of flood prediction and susceptibility mapping. Such methodology improves the state-of-the-art knowledge in this field and fills the gaps of traditional flood mapping techniques. Thus, the results of the study can provide crucial information for informed decision-making in the processes of designing flood control strategies and risk management. Full article
(This article belongs to the Special Issue Advancements in Remote Sensing and Artificial Intelligence for Geohazards)
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25 pages, 16688 KiB  
Article
Near-Real Prediction of Earthquake-Triggered Landslides on the Southeastern Margin of the Tibetan Plateau
by Aomei Zhang, Xianmin Wang, Chong Xu, Qiyuan Yang, Haixiang Guo and Dongdong Li
Remote Sens. 2024, 16(10), 1683; https://doi.org/10.3390/rs16101683 - 9 May 2024
Viewed by 1060
Abstract
Earthquake-triggered landslides (ETLs) feature large quantities, extensive distributions, and enormous losses to human lives and critical infrastructures. Near-real spatial prediction of ETLs can rapidly predict the locations of coseismic landslides just after a violent earthquake and is a vital technical support for emergency [...] Read more.
Earthquake-triggered landslides (ETLs) feature large quantities, extensive distributions, and enormous losses to human lives and critical infrastructures. Near-real spatial prediction of ETLs can rapidly predict the locations of coseismic landslides just after a violent earthquake and is a vital technical support for emergency response. However, near-real prediction of ETLs has always been a great challenge with relatively low accuracy. This work proposes an ensemble prediction model of EnPr by integrating machine learning tree models and a deep learning convolutional neural network. EnPr exhibits relatively strong prediction and generalization performance and achieves relatively accurate prediction of ETLs. Six great seismic events occurring from 2008 to 2022 on the southeastern margin of the Tibetan Plateau are selected to conduct ETL prediction. In a chronological order, the 2008 Ms 8.0 Wenchuan, 2010 Ms 7.1 Yushu, 2013 Ms 7.0 Lushan, and 2014 Ms 6.5 Ludian earthquakes are employed for model training and learning. The 2017 Ms 7.0 Jiuzhaigou and 2022 Ms 6.1 Lushan earthquakes are adopted for ETL prediction. The prediction accuracy merits of ACC and AUC attain 91.28% and 0.85, respectively, for the Jiuzhaigou earthquake. The values of ACC and AUC achieve 93.78% and 0.88, respectively, for the Lushan earthquake. The proposed EnPr algorithm outperforms the algorithms of XGBoost, random forest (RF), extremely randomized trees (ET), convolutional neural network (CNN), and Transformer. Moreover, this work reveals that seismic intensity, high and steep relief, pre-seismic fault tectonics, and pre-earthquake road construction have played significant roles in coseismic landslide occurrence and distribution. The EnPr model uses globally accessible open datasets and can therefore be used worldwide for new large seismic events in the future. Full article
(This article belongs to the Special Issue Advancements in Remote Sensing and Artificial Intelligence for Geohazards)
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23 pages, 6957 KiB  
Article
Study on Early Identification of Rainfall-Induced Accumulation Landslide Hazards in the Three Gorges Reservoir Area
by Zhen Wu, Runqing Ye, Shishi Yang, Tianlong Wen, Jue Huang and Yao Chen
Remote Sens. 2024, 16(10), 1669; https://doi.org/10.3390/rs16101669 - 8 May 2024
Cited by 1 | Viewed by 1185
Abstract
The early identification of potential hazards is crucial for landslide early warning and prevention and is a key focus and challenging issue in landslide disaster research. The challenges of traditional investigation and identification methods include identifying potential hazards of landslides triggered by heavy [...] Read more.
The early identification of potential hazards is crucial for landslide early warning and prevention and is a key focus and challenging issue in landslide disaster research. The challenges of traditional investigation and identification methods include identifying potential hazards of landslides triggered by heavy rainfall and mapping areas susceptible to landslides based on rainfall conditions. This article focuses on the problem of early identification of rainfall-induced accumulation landslide hazards and an early identification method is proposed, which is “first identifying the accumulation that is prone to landslides and then determining the associated rainfall conditions”. This method is based on identifying the distribution and thickness of accumulation, analyzing the rainfall conditions that trigger landslides with varying characteristics, and establishing rainfall thresholds for landslides with different accumulation characteristics, ultimately aiming to achieve early identification of accumulation landslide hazards. In this study, we focus on the Zigui section of the Three Gorges Reservoir as study the area, and eight main factors that influence the distribution and thickness of accumulation are extracted from multi-source data, then the relative thickness information extraction model of accumulation is established by using the BP neural network method. The accumulation distribution and relative thickness map of the study area are generated, and the study area is divided into rocky area (less than 1 m), thin (1 to 5 m), medium (5 to 10 m), and thick area (thicker than 10 m) according to accumulation thickness. Rainfall is a significant trigger for landslide hazards. It increases the weight of the sliding mass and decreases the shear strength of soil and rock layers, thus contributing to landslide events. Data on 101 rainfall-induced accumulation landslides in the Three Gorges Reservoir area and rainfall data for the 10 days prior to each landslide event were collected. The critical rainfall thresholds corresponding to a 90% probability of landslide occurrence with different characteristics were determined using the I-D threshold curve method. Prediction maps of accumulation landslide hazards under various rainfall conditions were generated by analyzing the rainfall threshold for landslides in the Three Gorges Reservoir area, serving as a basis for early identification of rainfall-induced accumulation landslides in the region. The research provides a method for the early identification of landslides caused by heavy rainfall, delineating landslide hazards under different rainfall conditions, and providing a basis for scientific responses, work arrangements, and disaster prevention and mitigation of landslides caused by heavy rainfall. Full article
(This article belongs to the Special Issue Advancements in Remote Sensing and Artificial Intelligence for Geohazards)
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