13 citations as recorded by crossref.

1. From spatio-temporal landslide susceptibility to landslide risk forecast T. Wang et al. 10.1016/j.gsf.2023.101765
2. Landslide susceptibility mapping using physics-guided machine learning: a case study of a debris flow event in Colorado Front Range T. Pei & T. Qiu 10.1007/s11440-024-02384-y
3. A case-based reasoning strategy of integrating case-level and covariate-level reasoning to automatically select covariates for spatial prediction Y. Wang et al. 10.1080/19475683.2024.2324398
4. An ensemble neural network approach for space–time landslide predictive modelling J. Lim et al. 10.1016/j.jag.2024.104037
5. Hazard Susceptibility Mapping with Machine and Deep Learning: A Literature Review A. Pugliese Viloria et al. 10.3390/rs16183374
6. Unsupervised active–transfer learning for automated landslide mapping Z. Wang & A. Brenning 10.1016/j.cageo.2023.105457
7. Assessing multi-hazard susceptibility to cryospheric hazards: Lesson learnt from an Alaskan example L. Elia et al. 10.1016/j.scitotenv.2023.165289
8. Feature adaptation for landslide susceptibility assessment in “no sample” areas Y. Su et al. 10.1016/j.gr.2024.03.002
9. Application of Enhanced YOLOX for Debris Flow Detection in Remote Sensing Images S. Ma et al. 10.3390/app14052158
10. Heterogeneous transfer learning considering feature representation and environmental consistency for landslide spatial prediction Z. Zhao et al. 10.1080/15481603.2024.2349343
11. Transfer Learning with Attributes for Improving the Landslide Spatial Prediction Performance in Sample-Scarce Area Based on Variational Autoencoder Generative Adversarial Network M. Lin et al. 10.3390/land12030525
12. Landslide susceptibility mapping using ensemble machine learning methods: a case study in Lombardy, Northern Italy Q. Xu et al. 10.1080/17538947.2024.2346263
13. Graph-Represented Broad Learning System for Landslide Susceptibility Mapping in Alpine-Canyon Region L. Xu et al. 10.3390/rs14122773