Machine learning based landslide susceptibility mapping models and GB-SAR based landslide deformation monitoring systems: Growth and evolution

Ganesh, Babitha; Vincent, Shweta; Pathan, Sameena; Benitez, Silvia Raquel Garcia

doi:10.1016/j.rsase.2022.100905

Cited by 13 publications

(6 citation statements)

References 113 publications

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“…Landslides are some of the most frequent natural hazards in the world's mountainous regions, and particularly in Vietnam (Khaliq et al, 2023;Vincent et al, 2023). Landslides have increased in inten- According to previous studies, the most important conditioning factors include (i) factors that make ground surface vulnerable to damage, often including intrinsic subsoil characteristics in areas of slope instability, and (ii) triggering factors for landslides, such as external factors like climate, hydrology and human impact (Liu et al, 2022;Yang et al, 2023).…”

Section: Discussionmentioning

confidence: 99%

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Landslide susceptibility prediction using machine learning and remote sensing: Case study in Thua Thien Hue province, Vietnam

Nguyen,

et al. 2023

Geological Journal

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Landslides lead to widespread devastation and significant loss of life in mountainous regions around the world. Susceptibility assessments can provide critical data to help decision‐makers, for example, local authorities and other organizations, mitigating the landslide risk, although the accuracy of existing studies needs to be improved. This study aims to assess landslide susceptibility in the Thua Thien Hue province of Vietnam using deep neural networks (DNNs) and swarm‐based optimization algorithms, namely Adam, stochastic gradient descent (SGD), Artificial Rabbits Optimization (ARO), Tuna Swarm Optimization (TSO), Sand Cat Swarm Optimization (SCSO), Honey Badger Algorithm (HBA), Marine Predators Algorithm (MPA) and Particle Swarm Optimization (PSO). The locations of 945 landslides occurring between 2012 and 2022, along with 14 conditioning factors, were used as input data to build the DNN and DNN‐hybrid models. The performance of the proposed models was evaluated using the statistical indices receiver operating characteristic curve, area under the curve (AUC), root mean square error, mean absolute error (MAE), R2 and accuracy. All proposed models had a high accuracy of prediction. The DNN‐MPA model had the highest AUC value (0.95), followed by DNN‐HBA (0.95), DNN‐ARO (0.95), DNN‐Adam (0.95), DNN‐SGD (0.95), DNN‐TSO (0.93), DNN‐PSO (0.9) and finally DNN‐SCSO (0.83). High‐precision models have identified that the majority of the western region of Thua Thien Hue province is very highly susceptible to landslides. Models like the aforementioned ones can support decision‐makers in updating large‐scale sustainable land‐use strategies.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Landslide susceptibility prediction using machine learning and remote sensing: Case study in Thua Thien Hue province, Vietnam

Nguyen,

et al. 2023

Geological Journal

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“…These advancements underscore the growing intersection between geospatial technologies and ML, highlighting how ML algorithms can refine the interpretation of SAR data for more effective landslide detection and monitoring. For instance, the literature review in [ 9 ] demonstrates the effectiveness of ML-based models and deep learning (DL)-based models in mapping landslide susceptibility, offering a comprehensive framework for predicting landslide-prone areas with high accuracy. Similarly, GB-SAR studies provide a detailed temporal and spatial analysis of ground movements that complement satellite-based observations, showcasing the utility of these technologies in closely monitoring landslide deformations.…”

Section: Introductionmentioning

confidence: 99%

Learning Ground Displacement Signals Directly from InSAR-Wrapped Interferograms

Moualla,

Rucci,

Naletto

et al. 2024

Sensors

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Monitoring ground displacements identifies potential geohazard risks early before they cause critical damage. Interferometric synthetic aperture radar (InSAR) is one of the techniques that can monitor these displacements with sub-millimeter accuracy. However, using the InSAR technique is challenging due to the need for high expertise, large data volumes, and other complexities. Accordingly, the development of an automated system to indicate ground displacements directly from the wrapped interferograms and coherence maps could be highly advantageous. Here, we compare different machine learning algorithms to evaluate the feasibility of achieving this objective. The inputs for the implemented machine learning models were pixels selected from the filtered-wrapped interferograms of Sentinel-1, using a coherence threshold. The outputs were the same pixels labeled as fast positive, positive, fast negative, negative, and undefined movements. These labels were assigned based on the velocity values of the measurement points located within the pixels. We used the Parallel Small Baseline Subset service of the European Space Agency’s GeoHazards Exploitation Platform to create the necessary interferograms, coherence, and deformation velocity maps. Subsequently, we applied a high-pass filter to the wrapped interferograms to separate the displacement signal from the atmospheric errors. We successfully identified the patterns associated with slow and fast movements by discerning the unique distributions within the matrices representing each movement class. The experiments included three case studies (from Italy, Portugal, and the United States), noted for their high sensitivity to landslides. We found that the Cosine K-nearest neighbor model achieved the best test accuracy. It is important to note that the test sets were not merely hidden parts of the training set within the same region but also included adjacent areas. We further improved the performance with pseudo-labeling, an approach aimed at evaluating the generalizability and robustness of the trained model beyond its immediate training environment. The lowest test accuracy achieved by the implemented algorithm was 80.1%. Furthermore, we used ArcGIS Pro 3.3 to compare the ground truth with the predictions to visualize the results better. The comparison aimed to explore indications of displacements affecting the main roads in the studied area.

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“…In recent years, deep learning models have efficiently utilized the availability of such multilayered data from sensors for various signal processing tasks. Different deep learning models with SAR and GBSAR databases are being used [14] to advance the understanding and potential of these systems, and in final applications help monitor and understand complex physical processes [15]. However, the drawback in these applications often lies in small datasets corresponding to a particular application.…”

Section: Introductionmentioning

confidence: 99%

Utilizing Polarization Diversity in GBSAR Data-Based Object Classification

Turčinović,

Kačan,

Bojanjac

et al. 2024

Sensors

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In recent years, the development of intelligent sensor systems has experienced remarkable growth, particularly in the domain of microwave and millimeter wave sensing, thanks to the increased availability of affordable hardware components. With the development of smart Ground-Based Synthetic Aperture Radar (GBSAR) system called GBSAR-Pi, we previously explored object classification applications based on raw radar data. Building upon this foundation, in this study, we analyze the potential of utilizing polarization information to improve the performance of deep learning models based on raw GBSAR data. The data are obtained with a GBSAR operating at 24 GHz with both vertical (VV) and horizontal (HH) polarization, resulting in two matrices (VV and HH) per observed scene. We present several approaches demonstrating the integration of such data into classification models based on a modified ResNet18 architecture. We also introduce a novel Siamese architecture tailored to accommodate the dual input radar data. The results indicate that a simple concatenation method is the most promising approach and underscore the importance of considering antenna polarization and merging strategies in deep learning applications based on radar data.

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Machine learning based landslide susceptibility mapping models and GB-SAR based landslide deformation monitoring systems: Growth and evolution

Cited by 13 publications

References 113 publications

Landslide susceptibility prediction using machine learning and remote sensing: Case study in Thua Thien Hue province, Vietnam

Landslide susceptibility prediction using machine learning and remote sensing: Case study in Thua Thien Hue province, Vietnam

Learning Ground Displacement Signals Directly from InSAR-Wrapped Interferograms

Utilizing Polarization Diversity in GBSAR Data-Based Object Classification

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