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Advance Geospatial Artificial Intelligence for Landslide Modeling, Prediction and Management

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A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (31 May 2020) | Viewed by 58631

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Special Issue Editors

Prof. Dr. Dieu Tien Bui

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Geographic Information System Group, Department of Business and IT, University of South-Eastern Norway, Notodden, Norway
Interests: sensors; LiDAR; GIS and geospatial technology; geo-hazards; artificial intelligence; soil engineering; marine geology; environmental managements
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Dr. Endre Før Gjermundsen

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Geographic Information System Group, Department of Business and IT, University of South-Eastern Norway, Notodden, Norway
Interests: quaternary geology; erosional processes; GIS; UAV photogrammetry; applied remote sensing; glaciology

Special Issue Information

Dear Colleagues,

Landslides are still one of the most destructive natural hazards worldwide, causing tremendous damage and fatalities each year.

This Special Issue encourages authors to share recent advances in landslide modeling, prediction, and management, with an emphasis on issues addressed by means of advanced geospatial artificial intelligence. This is an emerging scientific multidiscipline that combines innovations in geospatial technology, remote sensing, UAV photogrammetry, advanced artificial intelligence techniques (i.e., deep learning), data mining, hybrid and ensemble techniques, meta-heuristic optimization, and high-performance computing to extract knowledge from geospatial data.

We kindly invite the scientific community to contribute novel and original research to this Special Issue addressing at least one of the following topics:

Recent advances in geospatial technology, remote sensing, UAV photogrammetry, and machine learning for landslide detection and inventory mapping.
Recent advances in geospatial artificial intelligence for landslide modeling and prediction.
Recent advances in temporal prediction for landslides.
Recent advances in geospatial artificial intelligence for landslide risk management
Real-world case studies with findings of clear interest to the scientific community.

Finally, authors are encouraged to share codes and data so that their studies are easily reproducible and serve as the seeds for future improvements.

Prof. Dr. Dieu Tien Bui
Assoc. Prof. Dr. Endre Før Gjermundsen
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. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly 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 1700 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

landslides
geospatial technology
artificial intelligence
remote sensing
hybrid and ensemble
optimization

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Published Papers (11 papers)

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Research

26 pages, 12773 KiB

Open AccessArticle

Earth Observation and GIS-Based Analysis for Landslide Susceptibility and Risk Assessment

by Emmanouil Psomiadis, Nikos Charizopoulos, Nikolaos Efthimiou, Konstantinos X. Soulis and Ioannis Charalampopoulos

ISPRS Int. J. Geo-Inf. 2020, 9(9), 552; https://doi.org/10.3390/ijgi9090552 - 15 Sep 2020

Cited by 27 | Viewed by 4991

Abstract

Landslides can cause severe problems to the social and economic well-being. In order to effectively mitigate landslide hazards, the development of detailed susceptibility maps is required, towards implementing targeted risk management plans. This study aims to create detailed landslide susceptibility (LS) and landslide risk (LR) maps of the Sperchios River basin by applying an expert semi-quantitative approach that integrates the Geographic Information Systems (GIS)-based multicriteria analysis and Earth Observation (EO) data. Adopting the analytic hierarchy process (AHP) for a weighted linear combination (WLC) approach, eleven evaluation parameters were selected. The results were validated using a historic landslide database, enriched with new landslide locations mapped by satellite and aerial imagery interpretation and field surveys. Moreover, the landslide risk map of the area was also developed, based on the LS delineation, considering additionally the anthropogenic exposure and overall vulnerability of the area. The results showed that the most susceptible areas are located at the west and south-west regions of the basin. The synergistic use of GIS-based analysis and EO data can provide a useful tool for the design of natural hazards prevention policy at highly susceptible to risk landslide risk areas. Full article

(This article belongs to the Special Issue Advance Geospatial Artificial Intelligence for Landslide Modeling, Prediction and Management)

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18 pages, 29536 KiB

Open AccessArticle

Identification and Extraction of Geomorphological Features of Landslides Using Slope Units for Landslide Analysis

by Kai Wang, Hui Xu, Shaojie Zhang, Fangqiang Wei and Wanli Xie

ISPRS Int. J. Geo-Inf. 2020, 9(4), 274; https://doi.org/10.3390/ijgi9040274 - 22 Apr 2020

Cited by 10 | Viewed by 4798

Abstract

A slope unit is commonly used as calculation unit for regional landslide analysis. However, the capacity of the slope unit to reflect the geomorphological features of actual landslides still needs to be verified. This is because such accurate representation is critical to ensure the physical meaning of results from subsequent landslide stability analysis. This paper presents work conducted on landslides and slope extraction in two areas in China: The Jiangjia Gully area (Yunnan Province) and Fengjie County (Chongqing Municipality). Ground-based light detection and ranging (LiDAR) data are combined with field landslide terrace measurements to allow for the comparison of slope unit extraction methods (conventional vs. MIA-HSU) in terms of their ability to reflect the geomorphological features of shallow and deep-seated landslides. The results indicate that slope unit boundaries extracted by the conventional method do not match the geomorphological variations of actual landslides, and the method is therefore deficient in meaningfully extracting slope units for further landslide analysis. By contrast, slope units obtained using the MIA-HSU method accurately reflects the geomorphological features of both shallow and deep-seated landslides, and thus provides clearer geomorphological meaning and more reasonable calculation units for regional landslide assessment and prediction. Full article

(This article belongs to the Special Issue Advance Geospatial Artificial Intelligence for Landslide Modeling, Prediction and Management)

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24 pages, 24919 KiB

Open AccessArticle

A Multi-Objective Permanent Basic Farmland Delineation Model Based on Hybrid Particle Swarm Optimization

by Hua Wang, Wenwen Li, Wei Huang and Ke Nie

ISPRS Int. J. Geo-Inf. 2020, 9(4), 243; https://doi.org/10.3390/ijgi9040243 - 14 Apr 2020

Cited by 9 | Viewed by 2488

Abstract

The delimitation of permanent basic farmland is essentially a multi-objective optimization problem. The traditional demarcation methods cannot simultaneously take into account the requirements of cultivated land quality and the spatial layout of permanent basic farmland, and it cannot balance the relationship between agriculture and urban development. This paper proposed a multi-objective permanent basic farmland delimitation model based on an immune particle swarm optimization algorithm. The general rules for delineating the permanent basic farmland were defined in the model, and the delineation goals and constraints have been formally expressed. The model introduced the immune system concepts to complement the existing theory. This paper describes the coding and initialization methods for the algorithm, particle position and speed update mechanism, and fitness function design. We selected Xun County, Henan Province, as the research area and set up control experiments that aligned with the different targets and compared the performance of the three models of particle swarm optimization (PSO), artificial immune algorithm (AIA), and the improved AIA-PSO in solving multi-objective problems. The experiments proved the feasibility of the model. It avoided the adverse effects of subjective factors and promoted the scientific rationality of the results of permanent basic farmland delineation. Full article

(This article belongs to the Special Issue Advance Geospatial Artificial Intelligence for Landslide Modeling, Prediction and Management)

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29 pages, 7027 KiB

Open AccessArticle

Landslide Image Captioning Method Based on Semantic Gate and Bi-Temporal LSTM

by Wenqi Cui, Xin He, Meng Yao, Ziwei Wang, Jie Li, Yuanjie Hao, Weijie Wu, Huiling Zhao, Xianfeng Chen and Wei Cui

ISPRS Int. J. Geo-Inf. 2020, 9(4), 194; https://doi.org/10.3390/ijgi9040194 - 26 Mar 2020

Cited by 17 | Viewed by 4155

Abstract

When a landslide happens, it is important to recognize the hazard-affected bodies surrounding the landslide for the risk assessment and emergency rescue. In order to realize the recognition, the spatial relationship between landslides and other geographic objects such as residence, roads and schools needs to be defined. Comparing with semantic segmentation and instance segmentation that can only recognize the geographic objects separately, image captioning can provide richer semantic information including the spatial relationship among these objects. However, the traditional image captioning methods based on RNNs have two main shortcomings: the errors in the prediction process are often accumulated and the location of attention is not always accurate which would lead to misjudgment of risk. To handle these problems, a landslide image interpretation network based on a semantic gate and a bi-temporal long-short term memory network (SG-BiTLSTM) is proposed in this paper. In the SG-BiTLSTM architecture, a U-Net is employed as an encoder to extract features of the images and generate the mask maps of the landslides and other geographic objects. The decoder of this structure consists of two interactive long-short term memory networks (LSTMs) to describe the spatial relationship among these geographic objects so that to further determine the role of the classified geographic objects for identifying the hazard-affected bodies. The purpose of this research is to judge the hazard-affected bodies of the landslide (i.e., buildings and roads) through the SG-BiTLSTM network to provide geographic information support for emergency service. The remote sensing data was taken by Worldview satellite after the Wenchuan earthquake happened in 2008. The experimental results demonstrate that SG-BiTLSTM network shows remarkable improvements on the recognition of landslide and hazard-affected bodies, compared with the traditional LSTM (the Baseline Model), the BLEU1 of the SG-BiTLSTM is improved by 5.89%, the matching rate between the mask maps and the focus matrix of the attention is improved by 42.81%. In conclusion, the SG-BiTLSTM network can recognize landslides and the hazard-affected bodies simultaneously to provide basic geographic information service for emergency decision-making. Full article

(This article belongs to the Special Issue Advance Geospatial Artificial Intelligence for Landslide Modeling, Prediction and Management)

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19 pages, 13083 KiB

Open AccessArticle

Spatial Prediction of Landslide Susceptibility Based on GIS and Discriminant Functions

by Guirong Wang, Xi Chen and Wei Chen

ISPRS Int. J. Geo-Inf. 2020, 9(3), 144; https://doi.org/10.3390/ijgi9030144 - 29 Feb 2020

Cited by 47 | Viewed by 5213

Abstract

The areas where landslides occur frequently pose severe threats to the local population, which necessitates conducting regional landslide susceptibility mapping (LSM). In this study, four models including weight-of-evidence (WoE) and three WoE-based models, which were linear discriminant analysis (LDA), Fisher’s linear discriminant analysis (FLDA), and quadratic discriminant analysis (QDA), were used to obtain the LSM in the Nanchuan region of Chongqing, China. Firstly, a dataset was prepared from sixteen landslide causative factors, including eight topographic factors, three distance-related factors, and five environmental factors. A landslide inventory map including 298 landslide locations was also constructed and randomly divided with a ratio of 70:30 as training and validation data. Subsequently, the WoE method was used to estimate the relationship between landslides and the landslide causative factors, which assign a weight value to each class of causative factors. Finally, four models were applied using the training dataset, and the predictive performance of each model was compared using the validation datasets. The results showed that FLDA had a higher performance than the other three models according to the success rate curve (SRC) and prediction rate curve (PRC), illustrating that it could be considered a promising approach for landslide susceptibility mapping in the study area. Full article

(This article belongs to the Special Issue Advance Geospatial Artificial Intelligence for Landslide Modeling, Prediction and Management)

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17 pages, 12969 KiB

Open AccessArticle

Field Geological Investigations and Stability Analysis of Duanjiagou Landslide

by Xingang Wang, Tangdai Xia, Longju Zhang, Min Gao and Kang Cheng

ISPRS Int. J. Geo-Inf. 2020, 9(1), 23; https://doi.org/10.3390/ijgi9010023 - 1 Jan 2020

Cited by 6 | Viewed by 3303

Abstract

This paper analyses the stability of the Duanjiagou landslide on the Bazhong to Guangan Expressway K134–K135 segment in China. The Duanjiagou landslide took place on 4 November 2015. In order to discover the cause of the landslide, we carried out field geological investigations. The indoor physical property experiments were performed by taking the undisturbed soil sample from the borehole cores. To study the strength of the soil, we carried out a saturation direct shear test and saturation residual shear test on sliding zone soil samples. According to the physical properties of soil and the saturated shear strength parameters of sliding zone soil, the stability was analyzed by the landslide force transmission method and numerical simulation method. The results showed that in the initial sliding stage, the safety factor obtained by using the average value of saturated shear strength parameters was in good agreement with the field observation situation. The landslide was at an unstable state. The softening of soil and roadbed excavation at the foot of the slope are the main reasons for landslides. Full article

(This article belongs to the Special Issue Advance Geospatial Artificial Intelligence for Landslide Modeling, Prediction and Management)

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21 pages, 34717 KiB

Open AccessArticle

An Experimental Research on the Use of Recurrent Neural Networks in Landslide Susceptibility Mapping

by Begum Mutlu, Hakan A. Nefeslioglu, Ebru A. Sezer, M. Ali Akcayol and Candan Gokceoglu

ISPRS Int. J. Geo-Inf. 2019, 8(12), 578; https://doi.org/10.3390/ijgi8120578 - 11 Dec 2019

Cited by 46 | Viewed by 4995

Abstract

Natural hazards have a great number of influencing factors. Machine-learning approaches have been employed to understand the individual and joint relations of these factors. However, it is a challenging process for a machine learning algorithm to learn the relations of a large parameter space. In this circumstance, the success of the model is highly dependent on the applied parameter reduction procedure. As a state-of-the-art neural network model, representative learning assumes full responsibility of learning from feature extraction to prediction. In this study, a representative learning technique, recurrent neural network (RNN), was applied to a natural hazard problem. To that end, it aimed to assess the landslide problem by two objectives: Landslide susceptibility and inventory. Regarding the first objective, an empirical study was performed to explore the most convenient parameter set. In landslide inventory studies, the capability of the implemented RNN on predicting the subsequent landslides based on the events before a certain time was investigated respecting the resulting parameter set of the first objective. To evaluate the behavior of implemented neural models, receiver operating characteristic analysis was performed. Precision, recall, f-measure, and accuracy values were additionally measured by changing the classification threshold. Here, it was proposed that recall metric be utilized for an evaluation of landslide mapping. Results showed that the implemented RNN achieves a high estimation capability for landslide susceptibility. By increasing the network complexity, the model started to predict the exact label of the corresponding landslide initiation point instead of estimating the susceptibility level. Full article

(This article belongs to the Special Issue Advance Geospatial Artificial Intelligence for Landslide Modeling, Prediction and Management)

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35 pages, 8894 KiB

Open AccessArticle

Predicting Slope Stability Failure through Machine Learning Paradigms

by Dieu Tien Bui, Hossein Moayedi, Mesut Gör, Abolfazl Jaafari and Loke Kok Foong

ISPRS Int. J. Geo-Inf. 2019, 8(9), 395; https://doi.org/10.3390/ijgi8090395 - 4 Sep 2019

Cited by 84 | Viewed by 9408

Abstract

In this study, we employed various machine learning-based techniques in predicting factor of safety against slope failures. Different regression methods namely, multi-layer perceptron (MLP), Gaussian process regression (GPR), multiple linear regression (MLR), simple linear regression (SLR), support vector regression (SVR) were used. Traditional methods of slope analysis (e.g., first established in the first half of the twentieth century) used widely as engineering design tools. Offering more progressive design tools, such as machine learning-based predictive algorithms, they draw the attention of many researchers. The main objective of the current study is to evaluate and optimize various machine learning-based and multilinear regression models predicting the safety factor. To prepare training and testing datasets for the predictive models, 630 finite limit equilibrium analysis modelling (i.e., a database including 504 training datasets and 126 testing datasets) were employed on a single-layered cohesive soil layer. The estimated results for the presented database from GPR, MLR, MLP, SLR, and SVR were assessed by various methods. Firstly, the efficiency of applied models was calculated employing various statistical indices. As a result, obtained total scores 20, 35, 50, 10, and 35, respectively for GPR, MLR, MLP, SLR, and SVR, revealed that the MLP outperformed other machine learning-based models. In addition, SVR and MLR presented an almost equal accuracy in estimation, for both training and testing phases. Note that, an acceptable degree of efficiency was obtained for GPR and SLR models. However, GPR showed more precision. Following this, the equation of applied MLP and MLR models (i.e., in their optimal condition) was derived, due to the reliability of their results, to be used in similar slope stability problems. Full article

(This article belongs to the Special Issue Advance Geospatial Artificial Intelligence for Landslide Modeling, Prediction and Management)

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22 pages, 9350 KiB

Open AccessArticle

The Feasibility of Three Prediction Techniques of the Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System, and Hybrid Particle Swarm Optimization for Assessing the Safety Factor of Cohesive Slopes

by Hossein Moayedi, Dieu Tien Bui, Mesut Gör, Biswajeet Pradhan and Abolfazl Jaafari

ISPRS Int. J. Geo-Inf. 2019, 8(9), 391; https://doi.org/10.3390/ijgi8090391 - 4 Sep 2019

Cited by 81 | Viewed by 5431

Abstract

In this paper, a neuro particle-based optimization of the artificial neural network (ANN) is investigated for slope stability calculation. The results are also compared to another artificial intelligence technique of a conventional ANN and adaptive neuro-fuzzy inference system (ANFIS) training solutions. The database used with 504 training datasets (e.g., a range of 80%) and testing dataset consists of 126 items (e.g., 20% of the whole dataset). Moreover, variables of the ANN method (for example, nodes number for each hidden layer) and the algorithm of PSO-like swarm size and inertia weight are improved by utilizing a total of 28 (i.e., for the PSO-ANN) trial and error approaches. The key properties were fed as input, which were utilized via the analysis of OptumG2 finite element modelling (FEM), containing undrained cohesion stability of the baseline soil (C_u), angle of the original slope (β), and setback distance ratio (b/B) where the target is selected factor of safety. The estimated data for datasets of ANN, ANFIS, and PSO-ANN models were examined based on three determined statistical indexes. Namely, root mean square error (RMSE) and the coefficient of determination (R²). After accomplishing the analysis of sensitivity, considering 72 trials and errors of the neurons number, the optimized architecture of 4 × 6 × 1 was determined to the structure of the ANN model. As an outcome, the employed methods presented excellent efficiency, but based on the ranking method, the PSO-ANN approach might have slightly better efficiency in comparison to the algorithms of ANN and ANFIS. According to statistics, for the proper structure of PSO-ANN, the indexes of R² and RMSE values of 0.9996, and 0.0123, as well as 0.9994 and 0.0157, were calculated for the training and testing networks. Nevertheless, having the ANN model with six neurons for each hidden layer was formulized for further practical use. This study demonstrates the efficiency of the proposed neuro model of PSO-ANN in estimating the factor of safety compared to other conventional techniques. Full article

(This article belongs to the Special Issue Advance Geospatial Artificial Intelligence for Landslide Modeling, Prediction and Management)

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17 pages, 8205 KiB

Open AccessArticle

On the Use of Single-, Dual-, and Quad-Polarimetric SAR Observation for Landslide Detection

by Sang-Eun Park and Sun-Gu Lee

ISPRS Int. J. Geo-Inf. 2019, 8(9), 384; https://doi.org/10.3390/ijgi8090384 - 2 Sep 2019

Cited by 19 | Viewed by 4089

Abstract

Remote sensing technologies, particularly with Synthetic Aperture Radar (SAR) system, can provide timely and critical information to assess landslide distributions over large areas. Most space-borne SAR systems have been operating in different polarimetric modes to meet various operational requirements. This study aims to discuss how much detectability can be expected in the landslide map produced from the single-, dual-, and quad-polarization modes of observation. The experimental analysis of the characteristic changes of PALSAR-2 signals showed that quad-polarization parameters indicating signal depolarization properties revealed noticeable landslide-induced temporal changes for all local incidence angle ranges. To produce a landslide map, a simple change detection method based on characteristic scattering properties of landslide areas was proposed. The accuracy assessment results showed that the depolarization parameters, such as the co-pol coherence and polarizing contribution, can identify areas affected by landslides with a detection rate of 60%, and a false-alarm rate of 5%. On the other hand, the single- or dual-pol parameters can only be expected to provide half the accuracy with significant false-alarms in areas with temporal variations independent of landslides. Full article

(This article belongs to the Special Issue Advance Geospatial Artificial Intelligence for Landslide Modeling, Prediction and Management)

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25 pages, 13682 KiB

Open AccessArticle

A Convolutional Neural Network Architecture for Auto-Detection of Landslide Photographs to Assess Citizen Science and Volunteered Geographic Information Data Quality

by Recep Can, Sultan Kocaman and Candan Gokceoglu

ISPRS Int. J. Geo-Inf. 2019, 8(7), 300; https://doi.org/10.3390/ijgi8070300 - 15 Jul 2019

Cited by 61 | Viewed by 7363

Abstract

Several scientific processes benefit from Citizen Science (CitSci) and VGI (Volunteered Geographical Information) with the help of mobile and geospatial technologies. Studies on landslides can also take advantage of these approaches to a great extent. However, the quality of the collected data by both approaches is often questionable, and automated procedures to check the quality are needed for this purpose. In the present study, a convolutional neural network (CNN) architecture is proposed to validate landslide photos collected by citizens or nonexperts and integrated into a mobile- and web-based GIS environment designed specifically for a landslide CitSci project. The VGG16 has been used as the base model since it allows finetuning, and high performance could be achieved by selecting the best hyper-parameters. Although the training dataset was small, the proposed CNN architecture was found to be effective as it could identify the landslide photos with 94% precision. The accuracy of the results is sufficient for purpose and could even be improved further using a larger amount of training data, which is expected to be obtained with the help of volunteers. Full article

(This article belongs to the Special Issue Advance Geospatial Artificial Intelligence for Landslide Modeling, Prediction and Management)

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