14 citations as recorded by crossref.

1. PSLSA v2.0: An automatic Python package integrating machine learning models for regional landslide susceptibility assessment Z. Guo et al. https://doi.org/10.1016/j.envsoft.2025.106367
2. Hazard assessment modeling and software development of earthquake-triggered landslides in the Sichuan–Yunnan area, China X. Shao et al. https://doi.org/10.5194/gmd-16-5113-2023
3. Project ‘‘Mass Movements in Germany’’ and its implications for nationwide landslide susceptibility assessment J. Torizin et al. https://doi.org/10.1007/s10064-024-03691-0
4. From Mass Movements to Mitigation Measures: A Statistical Evaluation of Landslides in the Austrian Federal Railway Network H. Laimer https://doi.org/10.17738/ajes.2025.0016
5. Analytical ‘decisiveness’ as a robust measure of the absolute importance of landslide predisposing factors L. Li & H. Lan https://doi.org/10.1080/17538947.2024.2356161
6. A python system for regional landslide susceptibility assessment by integrating machine learning models and its application Z. Guo et al. https://doi.org/10.1016/j.heliyon.2023.e21542
7. Estimation of Shallow Landslide Susceptibility Incorporating the Impacts of Vegetation on Slope Stability H. Jiang et al. https://doi.org/10.1007/s13753-023-00507-9
8. An Efficient User-Friendly Integration Tool for Landslide Susceptibility Mapping Based on Support Vector Machines: SVM-LSM Toolbox W. Huang et al. https://doi.org/10.3390/rs14143408
9. An hourly shallow landslide warning model developed by combining automatic landslide spatial susceptibility and temporal rainfall threshold predictions Y. Cao et al. https://doi.org/10.1007/s11629-022-7370-1
10. Modeling and Assessment of Landslide Susceptibility of Dianchi Lake Watershed in Yunnan Plateau G. Bai et al. https://doi.org/10.3390/su152115221
11. An updated version of the SZ-plugin: From space to space–time data-driven modeling in QGIS G. Titti et al. https://doi.org/10.1016/j.jag.2025.104679
12. Advanced deep learning–based approaches for semantic segmentation in precise landslide detection and susceptibility assessment F. Rezaie et al. https://doi.org/10.1016/j.ecoinf.2025.103447
13. Landslide Susceptibility Mapping Using the Weight of Evidence Model: A Case Study on A Tropical Volcanic Region D. Nugraha et al. https://doi.org/10.47352/jmans.2774-3047.350
14. Co-seismic landslide hazard assessment and rapid mapping for post-seismic emergency disaster mitigation management at the national-scale Z. Gao et al. https://doi.org/10.3389/feart.2026.1742129