34 citations as recorded by crossref.

1. Measures of Spatial Autocorrelation Changes in Multitemporal SAR Images for Event Landslides Detection A. Mondini 10.3390/rs9060554
2. Landslide susceptibility mapping using ensemble machine learning methods: a case study in Lombardy, Northern Italy Q. Xu et al. 10.1080/17538947.2024.2346263
3. Automatic delineation of geomorphological slope units with <tt>r.slopeunits v1.0</tt> and their optimization for landslide susceptibility modeling M. Alvioli et al. 10.5194/gmd-9-3975-2016
4. Correlación de variables morfométricas para deslizamientos en la cuenca del río Combeima, Colombia G. Santa-Ramírez et al. 10.17230/ingciencia.16.31.7
5. Suitability assessment of global, continental and national digital elevation models for geomorphological analyses in Italy M. Zingaro et al. 10.1111/tgis.12845
6. Parameter-free delineation of slope units and terrain subdivision of Italy M. Alvioli et al. 10.1016/j.geomorph.2020.107124
7. Assessing and Improving Flood and Landslide Community Social Awareness and Engagement via a Web Platform: The Case of Italy D. Bignami et al. 10.1007/s13753-018-0199-0
8. Earthquake-induced landslides susceptibility evaluation: A case study from the Abruzzo region (Central Italy) C. Carabella et al. 10.1016/j.catena.2021.105729
9. Landslide susceptibility maps of Italy: Lesson learnt from dealing with multiple landslide types and the uneven spatial distribution of the national inventory M. Loche et al. 10.1016/j.earscirev.2022.104125
10. A review of statistically-based landslide susceptibility models P. Reichenbach et al. 10.1016/j.earscirev.2018.03.001
11. Presenting logistic regression-based landslide susceptibility results L. Lombardo & P. Mai 10.1016/j.enggeo.2018.07.019
12. Gender, age and circumstances analysis of flood and landslide fatalities in Italy P. Salvati et al. 10.1016/j.scitotenv.2017.08.064
13. Space-time landslide predictive modelling L. Lombardo et al. 10.1016/j.earscirev.2020.103318
14. Enhancing landslide susceptibility mapping incorporating landslide typology via stacking ensemble machine learning in Three Gorges Reservoir, China L. Yu et al. 10.1016/j.gsf.2024.101802
15. Window-Based Morphometric Indices as Predictive Variables for Landslide Susceptibility Models N. Barbosa et al. 10.3390/rs13030451
16. A spaceborne SAR-based procedure to support the detection of landslides G. Esposito et al. 10.5194/nhess-20-2379-2020
17. Impacts of past and future land changes on landslides in southern Italy S. Gariano et al. 10.1007/s10113-017-1210-9
18. A global landslide non-susceptibility map G. Jia et al. 10.1016/j.geomorph.2021.107804
19. Landslide Susceptibility Evaluation and Management Using Different Machine Learning Methods in The Gallicash River Watershed, Iran A. Arabameri et al. 10.3390/rs12030475
20. Synthetic libraries of urban landslide simulations to identify slope failure hotspots and drivers across spatial scales and landscapes E. Bozzolan et al. 10.1007/s10346-024-02327-4
21. Automatic mapping of event landslides at basin scale in Taiwan using a Montecarlo approach and synthetic land cover fingerprints A. Mondini et al. 10.1016/j.jag.2017.07.016
22. Topography-driven satellite imagery analysis for landslide mapping M. Alvioli et al. 10.1080/19475705.2018.1458050
23. Geoenvironmental conditioning of landsliding in river valleys of lowland regions and its significance in landslide susceptibility assessment: A case study in the Lower Vistula Valley, Northern Poland D. Grabowski et al. 10.1016/j.geomorph.2022.108490
24. Landslide mobilization rates: A global analysis and model J. Broeckx et al. 10.1016/j.earscirev.2019.102972
25. A Comprehensive Comparison of Stable and Unstable Area Sampling Strategies in Large-Scale Landslide Susceptibility Models Using Machine Learning Methods M. Sinčić et al. 10.3390/rs16162923
26. The 10 m-resolution TINITALY DEM as a trans-disciplinary basis for the analysis of the Italian territory: Current trends and new perspectives S. Tarquini & L. Nannipieri 10.1016/j.geomorph.2016.12.022
27. Inventory of Historical and Recent Earthquake-Triggered Landslides and Assessment of Related Susceptibility by GIS-Based Analytic Hierarchy Process: The Case of Cephalonia (Ionian Islands, Western Greece) S. Mavroulis et al. 10.3390/app12062895
28. Landslide susceptibility modeling using Reduced Error Pruning Trees and different ensemble techniques: Hybrid machine learning approaches B. Pham et al. 10.1016/j.catena.2018.12.018
29. Evaluating the Response of Hydrological Stress Indices Using the CHyM Model over a Wide Area in Central Italy A. Lombardi et al. 10.3390/hydrology9080139
30. Delineating Non-Susceptible Landslide Areas in China Based on Topographic Index and Quantile Non-Linear Model S. Ma et al. 10.3390/f15040678
31. Geographical landslide early warning systems F. Guzzetti et al. 10.1016/j.earscirev.2019.102973
32. Deep learning forecast of rainfall-induced shallow landslides A. Mondini et al. 10.1038/s41467-023-38135-y
33. National-scale assessment of railways exposure to rapid flow-like landslides I. Marchesini et al. 10.1016/j.enggeo.2024.107474
34. Project ‘‘Mass Movements in Germany’’ and its implications for nationwide landslide susceptibility assessment J. Torizin et al. 10.1007/s10064-024-03691-0