Special Issue "Advance Geospatial Artificial Intelligence for Landslide Modeling, Prediction and Management"

Special Issue Editors

Guest Editor
Prof. Dr. Dieu Tien Bui Website E-Mail
Geographic Information System Group, Department of Business and IT, University of South-Eastern Norway, Norway
Interests: sensors; LiDAR; GIS and geospatial technology; geo-hazards; artificial intelligence; soil engineering; marine geology; environmental managements
Guest Editor
Assoc. Prof. Dr. Endre Før Gjermundsen Website E-Mail
Geographic Information System Group, Department of Business and IT, University of South-Eastern Norway, 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:

  1. Recent advances in geospatial technology, remote sensing, UAV photogrammetry, and machine learning for landslide detection and inventory mapping.
  2. Recent advances in geospatial artificial intelligence for landslide modeling and prediction.
  3. Recent advances in temporal prediction for landslides.
  4. Recent advances in geospatial artificial intelligence for landslide risk management
  5. 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 papers will be 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 1000 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

Published Papers (4 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Open AccessArticle
Predicting Slope Stability Failure through Machine Learning Paradigms
ISPRS Int. J. Geo-Inf. 2019, 8(9), 395; https://doi.org/10.3390/ijgi8090395 - 04 Sep 2019
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 [...] Read more.
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
Show Figures

Figure 1

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
ISPRS Int. J. Geo-Inf. 2019, 8(9), 391; https://doi.org/10.3390/ijgi8090391 - 04 Sep 2019
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 [...] Read more.
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 (Cu), 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
Show Figures

Figure 1

Open AccessArticle
On the Use of Single-, Dual-, and Quad-Polarimetric SAR Observation for Landslide Detection
ISPRS Int. J. Geo-Inf. 2019, 8(9), 384; https://doi.org/10.3390/ijgi8090384 - 02 Sep 2019
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 [...] Read more.
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
Show Figures

Figure 1

Open AccessArticle
A Convolutional Neural Network Architecture for Auto-Detection of Landslide Photographs to Assess Citizen Science and Volunteered Geographic Information Data Quality
ISPRS Int. J. Geo-Inf. 2019, 8(7), 300; https://doi.org/10.3390/ijgi8070300 - 15 Jul 2019
Cited by 1
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 [...] Read more.
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
Show Figures

Figure 1

Back to TopTop