Articles | Volume 26, issue 1
https://doi.org/10.5194/nhess-26-487-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-26-487-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review article
| 
26 Jan 2026
Review article |
| 26 Jan 2026
| 26 Jan 2026
Review article: Deep learning for potential landslide identification: data, models, applications, challenges, and opportunities
Pan Jiang
School of Engineering and Technology, China University of Geosciences (Beijing), 100083, Beijing, China
Zhengjing Ma
School of Engineering and Technology, China University of Geosciences (Beijing), 100083, Beijing, China
Gang Mei
CORRESPONDING AUTHOR
School of Engineering and Technology, China University of Geosciences (Beijing), 100083, Beijing, China
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Cited
18 citations as recorded by crossref.
- A Decade of Deep Learning in Remote Sensing: A Systematic Review of Reviews A. Safonova et al. https://doi.org/10.1109/ACCESS.2026.3705849
- Improved UCTransNet by Integrating Pyramid Kernel Interaction with Triplet Attention for Identifying Multi-Scale Landslides from GF-2 Imagery M. Wang et al. https://doi.org/10.3390/rs18030492
- A novel association rule mining framework using weighted positive-negative sample contrast for analysis of landslide factor combinations J. Li et al. https://doi.org/10.1016/j.asr.2026.06.016
- Editorial: Monitoring, early warning and mitigation of natural and engineered slopes - volume V H. Qiu et al. https://doi.org/10.3389/feart.2026.1829640
- Rapid image recognition for construction worker hard hat compliance – a practical application of convolutional neural networks C. Kao et al. https://doi.org/10.1080/15623599.2026.2666870
- Potential Landslide Area Identification Method Based on Deep Generative Adversarial Reinforcement Learning (DGARL-LS) X. Liu et al. https://doi.org/10.3390/app16147027
- InSAR Techniques for Landslide Study: A Review of Methods, Challenges, and Emerging Trends H. Zhang et al. https://doi.org/10.3390/s26154667
- Regional-scale landslide detection along the Sichuan-Xizang transportation corridor using stacking InSAR and spatial clustering approaches Y. Yi et al. https://doi.org/10.1007/s10064-026-05062-3
- NestedMambaUNet: A Direction-Aware State Space Network for Landslide Mapping from Remote Sensing Images Z. Ma et al. https://doi.org/10.3390/rs18111722
- Kinematics-Guided Transformer for Early Warning of Slope Failures Using Embedded IoT Displacement Sensors B. Ji et al. https://doi.org/10.3390/app16041922
- Full-Scale Feature Pyramid Information Propagation for Postearthquake Landslide Detection H. Li et al. https://doi.org/10.1109/TGRS.2026.3699793
- Enhancing the Reliability, Performance, and Practical Value of Deep Learning Landslide Mapping via Pixel-Level Uncertainty Analysis S. Dong et al. https://doi.org/10.3390/rs18162825
- Multi-Method Identification and Spatiotemporal Evolution Analysis of Active Deformation Areas in the Binchang Mining Area L. Hu et al. https://doi.org/10.3390/rs18142339
- TRB-Net: Terrain-Residual and Boundary-Assisted Multimodal Martian Landslide Segmentation on a Local MMLSv2 Split Y. Li et al. https://doi.org/10.3390/rs18152638
- Landslide displacement prediction and interpretability analysis based on a graph spatiotemporal attention network N. Wang & M. Wang https://doi.org/10.3389/feart.2026.1870960
- Landslide Susceptibility Evaluation Based on Deep Learning and Imbalanced Sampling at Multi-Scale W. Chen et al. https://doi.org/10.3390/rs18152617
- AS-UNet: A Lightweight U-Net with Asymmetric Strip Attention and Joint Gating for RGB Optical Landslide Segmentation H. You et al. https://doi.org/10.3390/s26154820
- Landslide Susceptibility Mapping Using an Image–Tabular Joint Deep Learning Framework: A Case Study of the Tacheng Region, Xinjiang, China Q. Deng et al. https://doi.org/10.3390/rs18152436
18 citations as recorded by crossref.
- A Decade of Deep Learning in Remote Sensing: A Systematic Review of Reviews A. Safonova et al. https://doi.org/10.1109/ACCESS.2026.3705849
- Improved UCTransNet by Integrating Pyramid Kernel Interaction with Triplet Attention for Identifying Multi-Scale Landslides from GF-2 Imagery M. Wang et al. https://doi.org/10.3390/rs18030492
- A novel association rule mining framework using weighted positive-negative sample contrast for analysis of landslide factor combinations J. Li et al. https://doi.org/10.1016/j.asr.2026.06.016
- Editorial: Monitoring, early warning and mitigation of natural and engineered slopes - volume V H. Qiu et al. https://doi.org/10.3389/feart.2026.1829640
- Rapid image recognition for construction worker hard hat compliance – a practical application of convolutional neural networks C. Kao et al. https://doi.org/10.1080/15623599.2026.2666870
- Potential Landslide Area Identification Method Based on Deep Generative Adversarial Reinforcement Learning (DGARL-LS) X. Liu et al. https://doi.org/10.3390/app16147027
- InSAR Techniques for Landslide Study: A Review of Methods, Challenges, and Emerging Trends H. Zhang et al. https://doi.org/10.3390/s26154667
- Regional-scale landslide detection along the Sichuan-Xizang transportation corridor using stacking InSAR and spatial clustering approaches Y. Yi et al. https://doi.org/10.1007/s10064-026-05062-3
- NestedMambaUNet: A Direction-Aware State Space Network for Landslide Mapping from Remote Sensing Images Z. Ma et al. https://doi.org/10.3390/rs18111722
- Kinematics-Guided Transformer for Early Warning of Slope Failures Using Embedded IoT Displacement Sensors B. Ji et al. https://doi.org/10.3390/app16041922
- Full-Scale Feature Pyramid Information Propagation for Postearthquake Landslide Detection H. Li et al. https://doi.org/10.1109/TGRS.2026.3699793
- Enhancing the Reliability, Performance, and Practical Value of Deep Learning Landslide Mapping via Pixel-Level Uncertainty Analysis S. Dong et al. https://doi.org/10.3390/rs18162825
- Multi-Method Identification and Spatiotemporal Evolution Analysis of Active Deformation Areas in the Binchang Mining Area L. Hu et al. https://doi.org/10.3390/rs18142339
- TRB-Net: Terrain-Residual and Boundary-Assisted Multimodal Martian Landslide Segmentation on a Local MMLSv2 Split Y. Li et al. https://doi.org/10.3390/rs18152638
- Landslide displacement prediction and interpretability analysis based on a graph spatiotemporal attention network N. Wang & M. Wang https://doi.org/10.3389/feart.2026.1870960
- Landslide Susceptibility Evaluation Based on Deep Learning and Imbalanced Sampling at Multi-Scale W. Chen et al. https://doi.org/10.3390/rs18152617
- AS-UNet: A Lightweight U-Net with Asymmetric Strip Attention and Joint Gating for RGB Optical Landslide Segmentation H. You et al. https://doi.org/10.3390/s26154820
- Landslide Susceptibility Mapping Using an Image–Tabular Joint Deep Learning Framework: A Case Study of the Tacheng Region, Xinjiang, China Q. Deng et al. https://doi.org/10.3390/rs18152436
Saved (final revised paper)
Latest update: 07 Oct 2026
Short summary
In order to elucidate the potential for integrating deep learning with potential landslide identification, this paper focuses on four key dimensions: (1) Summarising data sources for potential landslide identification. (2) Compare the roles of commonly used deep learning models. (3) Analyse the practical applications of deep learning in early landslide detection. (4) Investigate key challenges and propose future priorities for potential landslide identification.
In order to elucidate the potential for integrating deep learning with potential landslide...
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