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An Optimized Heterogeneous Ensemble Learning Algorithm for InSAR Landslide Susceptibility Mapping Based on the Adaptive Sampling Strategy.

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Title: An Optimized Heterogeneous Ensemble Learning Algorithm for InSAR Landslide Susceptibility Mapping Based on the Adaptive Sampling Strategy.
Authors: Li, Lu1 (AUTHOR) lilu21@mails.ucas.ac.cn, Cheng, Hongyan1,2 (AUTHOR), Guo, Yuhua1 (AUTHOR), Liu, Shangqiang1,2 (AUTHOR), Yin, Jianyong1 (AUTHOR), Wang, Jili2 (AUTHOR)
Source: Remote Sensing. Jun2026, Vol. 18 Issue 12, p1985. 28p.
Subjects: Adaptive sampling (Statistics), Ensemble learning, Machine learning, Landslide hazard analysis, Monte Carlo method, Radar interferometry, Spatial analysis (Statistics)
Geographic Terms: Jinsha River (China)
Abstract: Highlights: What are the main findings? An adaptive sampling strategy that integrates InSAR-derived information with Getis–Ord Gi-based hotspot analysis was proposed to refine the samples. A Monte Carlo-based frequency ratio analysis was proposed to address low computational efficiency and optimize conditioning factors. What are the implications of the main findings? Surface deformation information serves as an effective indicator for refining non-stable samples related to geological disasters and generating high-quality training samples for model learning. The proposed framework provides support for regional geohazard susceptibility assessment under dynamic environments. Landslide susceptibility algorithms demonstrate high reliability in quantifying the likelihood of landslide occurrence. However, traditional methods are often limited by computationally intensive sampling strategies and models with limited adaptability. In this study, we propose an adaptive sampling strategy based on hotspot analysis to enhance the reliability of the generated samples. Additionally, we develop an improved meta-ensemble (IME) stacking-based heterogeneous framework for landslide susceptibility assessment by integrating a support vector machine (SVM), random forest (RF), and XGBoost. To further reduce factor complexity, a Monte Carlo-based frequency ratio analysis is employed. The Baihetan Reservoir area along the Jinsha River was selected as the study area. A total of 26 conditioning factors were considered, supplemented by 120 Sentinel-1A images to cover the study area. The proposed sampling strategy was then used to generate high-quality samples. Finally, to evaluate the performance of the proposed method, the proposed ensemble learning framework was applied to assess landslide susceptibility with eight models using five evaluation metrics. The experimental results demonstrated that: (1) the adaptive sampling strategy improved both the quantity and quality of the training samples; (2) the adoption of the Monte Carlo strategy increased the sample partitioning rate; and (3) despite the formally highest IME metrics, the inclusion of InSAR information did not lead to a statistically significant improvement in the forecast compared to the high-quality basic sampling strategy. Overall, the proposed methodology provides valuable support for regional geohazard susceptibility assessment in dynamic environments. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: An Optimized Heterogeneous Ensemble Learning Algorithm for InSAR Landslide Susceptibility Mapping Based on the Adaptive Sampling Strategy.
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Jun2026, Vol. 18 Issue 12, p1985. 28p.
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  Data: <searchLink fieldCode="DE" term="%22Adaptive+sampling+%28Statistics%29%22">Adaptive sampling (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Ensemble+learning%22">Ensemble learning</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Landslide+hazard+analysis%22">Landslide hazard analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Monte+Carlo+method%22">Monte Carlo method</searchLink><br /><searchLink fieldCode="DE" term="%22Radar+interferometry%22">Radar interferometry</searchLink><br /><searchLink fieldCode="DE" term="%22Spatial+analysis+%28Statistics%29%22">Spatial analysis (Statistics)</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Jinsha+River+%28China%29%22">Jinsha River (China)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Highlights: What are the main findings? An adaptive sampling strategy that integrates InSAR-derived information with Getis–Ord Gi-based hotspot analysis was proposed to refine the samples. A Monte Carlo-based frequency ratio analysis was proposed to address low computational efficiency and optimize conditioning factors. What are the implications of the main findings? Surface deformation information serves as an effective indicator for refining non-stable samples related to geological disasters and generating high-quality training samples for model learning. The proposed framework provides support for regional geohazard susceptibility assessment under dynamic environments. Landslide susceptibility algorithms demonstrate high reliability in quantifying the likelihood of landslide occurrence. However, traditional methods are often limited by computationally intensive sampling strategies and models with limited adaptability. In this study, we propose an adaptive sampling strategy based on hotspot analysis to enhance the reliability of the generated samples. Additionally, we develop an improved meta-ensemble (IME) stacking-based heterogeneous framework for landslide susceptibility assessment by integrating a support vector machine (SVM), random forest (RF), and XGBoost. To further reduce factor complexity, a Monte Carlo-based frequency ratio analysis is employed. The Baihetan Reservoir area along the Jinsha River was selected as the study area. A total of 26 conditioning factors were considered, supplemented by 120 Sentinel-1A images to cover the study area. The proposed sampling strategy was then used to generate high-quality samples. Finally, to evaluate the performance of the proposed method, the proposed ensemble learning framework was applied to assess landslide susceptibility with eight models using five evaluation metrics. The experimental results demonstrated that: (1) the adaptive sampling strategy improved both the quantity and quality of the training samples; (2) the adoption of the Monte Carlo strategy increased the sample partitioning rate; and (3) despite the formally highest IME metrics, the inclusion of InSAR information did not lead to a statistically significant improvement in the forecast compared to the high-quality basic sampling strategy. Overall, the proposed methodology provides valuable support for regional geohazard susceptibility assessment in dynamic environments. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.3390/rs18121985
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 28
        StartPage: 1985
    Subjects:
      – SubjectFull: Adaptive sampling (Statistics)
        Type: general
      – SubjectFull: Ensemble learning
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Landslide hazard analysis
        Type: general
      – SubjectFull: Monte Carlo method
        Type: general
      – SubjectFull: Radar interferometry
        Type: general
      – SubjectFull: Spatial analysis (Statistics)
        Type: general
      – SubjectFull: Jinsha River (China)
        Type: general
    Titles:
      – TitleFull: An Optimized Heterogeneous Ensemble Learning Algorithm for InSAR Landslide Susceptibility Mapping Based on the Adaptive Sampling Strategy.
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            NameFull: Li, Lu
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            NameFull: Cheng, Hongyan
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            NameFull: Guo, Yuhua
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            NameFull: Liu, Shangqiang
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            NameFull: Yin, Jianyong
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            – D: 15
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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