14 citations as recorded by crossref.

1. Sediment connectivity as a key to understand geomorphic effects of the Storm Alex in two mountain catchments of the Mediterranean Alps (Italy) F. Ardizzone et al. https://doi.org/10.1016/j.geomorph.2024.109176
2. Bridging the Gap Between Active Faulting and Deformation Across Normal-Fault Systems in the Central–Southern Apennines (Italy): Multi-Scale and Multi-Source Data Analysis M. Battistelli et al. https://doi.org/10.3390/rs17142491
3. Multi-temporal morphological analysis coupled to seismic survey of a mass movement from southern Italy: a combined tool to unravel the history of complex slow-moving landslides D. Gioia et al. https://doi.org/10.1007/s11069-024-06751-6
4. RER2023: the landslide inventory dataset of the May 2023 Emilia-Romagna meteorological event M. Berti et al. https://doi.org/10.5194/essd-17-1055-2025
5. Enhancing machine learning-based gully erosion susceptibility modeling using a prediction-oriented spatial cross-validation framework E. Mekonnen et al. https://doi.org/10.1016/j.envc.2026.101418
6. New Paradigms for Geomorphological Mapping: A Multi-Source Approach for Landscape Characterization M. Cignetti et al. https://doi.org/10.3390/rs17040581
7. Revitalization Potential of Marginal Areas for Sustainable Rural Development in the Puglia Region, Southern Italy: Part I: A Review S. Alhajj Ali et al. https://doi.org/10.3390/agronomy14030431
8. A geodatabase of historical landslide events occurring in the highly urbanized volcanic area of Campi Flegrei, Italy G. Esposito & F. Matano https://doi.org/10.5194/essd-15-1133-2023
9. Moving beyond single slope quantitative analysis: A 3D slope stability assessment at urban scale A. Ugenti et al. https://doi.org/10.1016/j.enggeo.2024.107841
10. Sustainable Landslide Risk Assessment in Zonguldak Province Using AHP and Artificial Intelligence: Integration with InSAR and Inventory Data S. Kutoglu & D. Arca https://doi.org/10.3390/su18094263
11. Transformer, more than meets the eye: A deep learning approach to integrate rainfall time-series in multi-type landslide probability modelling R. Rani et al. https://doi.org/10.1016/j.gsf.2026.102295
12. Size scaling of large landslides from incomplete inventories O. Korup et al. https://doi.org/10.5194/nhess-24-3815-2024
13. LMHLD: A Large-Scale Multisource High-Resolution Landslide Dataset for Landslide Detection Based on Deep Learning G. Liu et al. https://doi.org/10.1109/TGRS.2025.3619062
14. Landslide susceptibility zoning through physically-based limit equilibrium method modelling F. Mevoli et al. https://doi.org/10.1016/j.catena.2025.109726