184 citations as recorded by crossref.

1. Efficient and automatic extraction of slope units based on multi-scale segmentation method for landslide assessments F. Huang et al. 10.1007/s10346-021-01756-9
2. Using R packages 'tmap', 'raster' and 'ggmap' for cartographic visualization: An example of dem-based terrain modelling of Italy, Apennine Peninsula P. Lemenkova 10.5937/zrgfub2068099L
3. Land surface diversity: A geomorphodiversity index of Italy M. Burnelli et al. 10.1002/esp.5679
4. An ensemble neural network approach for space–time landslide predictive modelling J. Lim et al. 10.1016/j.jag.2024.104037
5. Volume Estimation of Landslide Affected Soil Moisture Using TRIGRS: A Case Study of Longxi River Small Watershed in Wenchuan Earthquake Zone, China T. Sun et al. 10.3390/w13010071
6. Geostatistical Modeling to Capture Seismic‐Shaking Patterns From Earthquake‐Induced Landslides L. Lombardo et al. 10.1029/2019JF005056
7. Different Approaches to Use Morphometric Attributes in Landslide Susceptibility Mapping Based on Meso-Scale Spatial Units: A Case Study in Rio de Janeiro (Brazil) V. Canavesi et al. 10.3390/rs12111826
8. Impact of sampling for landslide susceptibility assessment using interpretable machine learning models B. Wu et al. 10.1007/s10064-024-03980-8
9. Hazard zoning for spatial planning using GIS-based landslide susceptibility assessment: a new hybrid integrated data-driven and knowledge-based model Q. Ashournejad et al. 10.1007/s12517-019-4236-0
10. Insights into geospatial heterogeneity of landslide susceptibility based on the SHAP-XGBoost model J. Zhang et al. 10.1016/j.jenvman.2023.117357
11. A python system for regional landslide susceptibility assessment by integrating machine learning models and its application Z. Guo et al. 10.1016/j.heliyon.2023.e21542
12. How can statistical and artificial intelligence approaches predict piping erosion susceptibility? M. Hosseinalizadeh et al. 10.1016/j.scitotenv.2018.07.396
13. Landslide susceptibility mapping of Darjeeling Himalaya, India using index of entropy (IOE) model S. Mondal & S. Mandal 10.1007/s12518-018-0248-9
14. Study on the spatial distribution pattern of correlation between surface deformation and reservoir water level in the Three Gorges Reservoir area H. Sang et al. 10.1007/s10346-024-02356-z
15. Assessing locations susceptible to shallow landslide initiation during prolonged intense rainfall in the Lares, Utuado, and Naranjito municipalities of Puerto Rico R. Baum et al. 10.5194/nhess-24-1579-2024
16. Preparing first-time slope failures hazard maps: from pixel-based to slope unit-based G. Domènech et al. 10.1007/s10346-019-01279-4
17. Administrative boundaries and urban areas in Italy: A perspective from scaling laws M. Alvioli 10.1016/j.landurbplan.2020.103906
18. Framework for susceptibility analysis of layered rock slopes considering the dimensions of the mapping units and geological data resolution at various map scales C. Lin et al. 10.1016/j.enggeo.2018.10.004
19. A novel QLattice‐based whitening machine learning model of landslide susceptibility mapping D. Sun et al. 10.1002/esp.5675
20. PSO-SLIC algorithm: A novel automated method for the generation of high-homogeneity slope units using DEM data Y. Li et al. 10.1016/j.geomorph.2024.109367
21. High-resolution Bayesian mapping of landslide hazard with unobserved trigger event T. Opitz et al. 10.1214/21-AOAS1561
22. Automatic landslide prioritization at regional scale through PS-InSAR cluster analysis and socio-economic impacts M. Zocchi et al. 10.1016/j.rsase.2024.101414
23. Identification and Extraction of Geomorphological Features of Landslides Using Slope Units for Landslide Analysis K. Wang et al. 10.3390/ijgi9040274
24. Impact of landslide on geoheritage: Opportunities through integration, geomorphological classification and machine learning M. Al’Afif et al. 10.1016/j.ijgeop.2024.05.002
25. Assessment of earthquake-induced landslide inventories and susceptibility maps using slope unit-based logistic regression and geospatial statistics B. Pokharel et al. 10.1038/s41598-021-00780-y
26. Space–time landslide hazard modeling via Ensemble Neural Networks A. Dahal et al. 10.5194/nhess-24-823-2024
27. Mapping Landslides on EO Data: Performance of Deep Learning Models vs. Traditional Machine Learning Models N. Prakash et al. 10.3390/rs12030346
28. Physically based morphostructural land surface segmentation: Case of the Alps and Western Carpathians P. Bandura et al. 10.1111/tgis.12847
29. Mapping debris flow susceptibility based on watershed unit and grid cell unit: a comparison study S. Qin et al. 10.1080/19475705.2019.1604572
30. National-scale assessment of railways exposure to rapid flow-like landslides I. Marchesini et al. 10.1016/j.enggeo.2024.107474
31. Improved landslide susceptibility assessment: A new negative sample collection strategy and a comparative analysis of zoning methods J. Wang et al. 10.1016/j.ecolind.2024.112948
32. The influence of the inventory on the determination of the rainfall-induced shallow landslides susceptibility using generalized additive models M. Bordoni et al. 10.1016/j.catena.2020.104630
33. The dynamic threat from landslides following large continental earthquakes K. Arrell et al. 10.1371/journal.pone.0308444
34. Aras Nehri havzasında lojistik regresyon yöntemiyle kayma ve akma tip heyelan duyarlılığı değerlendirmesi M. Cihangir 10.17211/tcd.1475065
35. The influence of land use and land cover change on landslide susceptibility: a case study in Zhushan Town, Xuan'en County (Hubei, China) L. Chen et al. 10.5194/nhess-19-2207-2019
36. A global landslide non-susceptibility map G. Jia et al. 10.1016/j.geomorph.2021.107804
37. Rapid landslide risk zoning toward multi-slope units of the Neikuihui tribe for preliminary disaster management C. Chung & Z. Li 10.5194/nhess-22-1777-2022
38. PHyL v1.0: A parallel, flexible, and advanced software for hydrological and slope stability modeling at a regional scale G. Chen et al. 10.1016/j.envsoft.2023.105882
39. Capturing the footprints of ground motion in the spatial distribution of rainfall-induced landslides H. Tanyaş et al. 10.1007/s10064-021-02238-x
40. Semi-automated classification of layered rock slopes using digital elevation model and geological map H. Shang et al. 10.1515/geo-2022-0526
41. Landslide hazard spatiotemporal prediction based on data-driven models: Estimating where, when and how large landslide may be Z. Fang et al. 10.1016/j.jag.2023.103631
42. Space–Time Landslide Susceptibility Modeling Based on Data-Driven Methods Z. Fang et al. 10.1007/s11004-023-10105-6
43. Regional early warning model for rainfall induced landslide based on slope unit in Chongqing, China S. Liu et al. 10.1016/j.enggeo.2024.107464
44. Landslide susceptibility mapping (LSM) based on different boosting and hyperparameter optimization algorithms: A case of Wanzhou District, China D. Sun et al. 10.1016/j.jrmge.2023.09.037
45. Towards physics-informed neural networks for landslide prediction A. Dahal & L. Lombardo 10.1016/j.enggeo.2024.107852
46. Parameter-free delineation of slope units and terrain subdivision of Italy M. Alvioli et al. 10.1016/j.geomorph.2020.107124
47. Temporal Variations in Landslide Distributions Following Extreme Events: Implications for Landslide Susceptibility Modeling J. Jones et al. 10.1029/2021JF006067
48. Uncertainty analysis of non-landslide sample selection in landslide susceptibility prediction using slope unit-based machine learning models Z. Chang et al. 10.1016/j.gr.2023.02.007
49. Landslide Susceptibility Mapping by Comparing GIS-Based Bivariate Methods: A Focus on the Geomorphological Implication of the Statistical Results L. Coco et al. 10.3390/rs13214280
50. A thermo-hydro-mechanical approach to soil slope stability under climate change G. Scaringi & M. Loche 10.1016/j.geomorph.2022.108108
51. Improved landslide prediction by considering continuous and discrete spatial dependency Z. Fang et al. 10.1007/s10346-024-02431-5
52. Multi-source deep-learning approach for automatic geomorphological mapping: the case of glacial moraines I. Rocamora et al. 10.1080/10095020.2023.2292587
53. Landslide susceptibility assessment using different slope units based on the evidential belief function model Z. Chen et al. 10.1080/10106049.2019.1582716
54. Reliable assessment approach of landslide susceptibility in broad areas based on optimal slope units and negative samples involving priori knowledge X. Fu et al. 10.1080/17538947.2022.2159549
55. INSPIRE standards as a framework for artificial intelligence applications: a landslide example G. Roberti et al. 10.5194/nhess-20-3455-2020
56. Rockfall Magnitude-Frequency Relationship Based on Multi-Source Data from Monitoring and Inventory M. Janeras et al. 10.3390/rs15081981
57. The influence of DEM spatial resolution on landslide susceptibility mapping in the Baxie River basin, NW China Z. Chen et al. 10.1007/s11069-020-03899-9
58. Object-based image analysis for extracting regional slope units and its application Y. Zeng et al. 10.1007/s12145-024-01408-1
59. Modeling landslide susceptibility using alternating decision tree and support vector Z. Chen et al. 10.1007/s44195-024-00074-6
60. Regional earthquake-induced landslide assessments for use in seismic risk analyses of distributed gas infrastructure systems O. Ojomo et al. 10.1016/j.enggeo.2024.107664
61. Distribution-agnostic landslide hazard modelling via Graph Transformers G. Belvederesi et al. 10.1016/j.envsoft.2024.106231
62. Space-time modeling of cascading hazards: Chaining wildfires, rainfall and landslide events through machine learning M. Di Napoli et al. 10.1016/j.catena.2024.108452
63. Spatial modeling of multi-hazard threat to cultural heritage sites L. Lombardo et al. 10.1016/j.enggeo.2020.105776
64. Explainable artificial intelligence in geoscience: A glimpse into the future of landslide susceptibility modeling A. Dahal & L. Lombardo 10.1016/j.cageo.2023.105364
65. Loess terrain segmentation from digital elevation models based on the region growth method H. Zhu et al. 10.1080/02723646.2017.1342215
66. Mapping the susceptibility of rainfall and earthquake triggered landslides along China–Nepal highways K. Gnyawali et al. 10.1007/s10064-019-01583-2
67. Shifting from traditional landslide occurrence modeling to scenario estimation with a “glass-box” machine learning F. Caleca et al. 10.1016/j.scitotenv.2024.175277
68. The (f)utility to account for pre-failure topography in data-driven landslide susceptibility modelling S. Steger et al. 10.1016/j.geomorph.2020.107041
69. Improvement of landslide hazard assessments for regulatory zoning in France: STATE–OF–THE-ART perspectives and considerations Y. Thiery et al. 10.1016/j.ijdrr.2020.101562
70. Physical geomorphometry for elementary land surface segmentation and digital geomorphological mapping J. Minár et al. 10.1016/j.earscirev.2023.104631
71. Incorporating Effects of Slope Units and Sliding Areas into Seismically Induced Landslide Risk Modeling in Tectonically Active Mountainous Areas H. Wu et al. 10.3390/rs16183517
72. Landslide susceptibility mapping using statistical bivariate models and their hybrid with normalized spatial-correlated scale index and weighted calibrated landslide potential model Z. Chen et al. 10.1007/s12665-021-09603-9
73. Event-based rainfall-induced landslide inventories and rainfall thresholds for Malawi P. Niyokwiringirwa et al. 10.1007/s10346-023-02203-7
74. Landslide Susceptibility Mapping in Terms of the Slope-Unit or Raster-Unit, Which is Better? S. Ma et al. 10.1007/s12583-021-1407-1
75. Effective surveyed area and its role in statistical landslide susceptibility assessments T. Bornaetxea et al. 10.5194/nhess-18-2455-2018
76. A mechanistic approach to include climate change and unplanned urban sprawl in landslide susceptibility maps E. Bozzolan et al. 10.1016/j.scitotenv.2022.159412
77. Modelling InSAR-derived hillslope velocities with multivariate statistics: A first attempt to generate interpretable predictions K. He et al. 10.1016/j.rse.2023.113518
78. Enhancing social vulnerability assessment with energy resilience: A comprehensive study of the Netherlands W. Song et al. 10.1016/j.scs.2024.105251
79. Development of a region-partitioning method for debris flow susceptibility mapping S. Qiao et al. 10.1007/s11629-020-6497-1
80. Modelling post‐earthquake cascading hazards: Changing patterns of landslide runout following the 2015 Gorkha earthquake, Nepal M. Kincey et al. 10.1002/esp.5501
81. Landslide Susceptibility Mapping Using the Slope Unit for Southeastern Helong City, Jilin Province, China: A Comparison of ANN and SVM C. Yu & J. Chen 10.3390/sym12061047
82. Implementing landslide path dependency in landslide susceptibility modelling J. Samia et al. 10.1007/s10346-018-1024-y
83. Landform classification: a high-performing mapping unit partitioning tool for landslide susceptibility assessment—a test in the Imera River basin (northern Sicily, Italy) C. Martinello et al. 10.1007/s10346-021-01781-8
84. Integrated risk assessment of landslide in karst terrains: Advancing landslides management in Beiliu City, China M. Chang et al. 10.1016/j.jag.2024.104046
85. A SHAP-enhanced XGBoost model for interpretable prediction of coseismic landslides H. Wen et al. 10.1016/j.asr.2024.07.013
86. Susceptibility Assessment for Landslide Initiated along Power Transmission Lines S. Liu et al. 10.3390/rs13245068
87. An improved time series SAR interferometry (TSInSAR) for investigating earthquake-induced active unstable slopes (AUS) in Pakistan N. Shahzad & X. Ding 10.1080/01431161.2024.2391106
88. Rockfall susceptibility and network-ranked susceptibility along the Italian railway M. Alvioli et al. 10.1016/j.enggeo.2021.106301
89. Regional susceptibility assessments with heterogeneous landslide information: Slope unit- vs. pixel-based approach L. Jacobs et al. 10.1016/j.geomorph.2020.107084
90. Rainfall-induced landslides assessment in the Fengjie County, Three-Gorge reservoir area, China K. Wang & S. Zhang 10.1007/s11069-021-04691-z
91. On the estimation of landslide intensity, hazard and density via data-driven models M. Di Napoli et al. 10.1007/s11069-023-06153-0
92. 基于知识图谱的滑坡易发性评价文献综述及研究进展 F. Guo et al. 10.3799/dqkx.2023.058
93. Analyzing the posterior predictive capability and usability of landslide susceptibility maps: a case of Kerala, India T. Pareek et al. 10.1007/s10346-024-02389-4
94. Enhancing Landslide Susceptibility Mapping by Integrating Neighboring Information in Slope Units: A Spatial Logistic Regression L. Li et al. 10.3390/rs16234475
95. Slope-Unit Scale Landslide Susceptibility Mapping Based on the Random Forest Model in Deep Valley Areas H. Deng et al. 10.3390/rs14174245
96. A health data led approach for assessing potential health benefits of green and blue spaces: Lessons from an Irish case study O. Arodudu et al. 10.1016/j.jenvman.2023.118758
97. Seismically induced rockfall hazard from a physically based model and ground motion scenarios in Italy M. Alvioli et al. 10.1016/j.geomorph.2023.108652
98. A New Parameter-Free Slope Unit Division Method That Integrates Terrain Factors P. Li et al. 10.3390/app142311279
99. Earthquake-triggered landslide susceptibility in Italy by means of Artificial Neural Network G. Amato et al. 10.1007/s10064-023-03163-x
100. Landslide Susceptibility Mapping along a Rapidly Uplifting River Valley of the Upper Jinsha River, Southeastern Tibetan Plateau, China X. Sun et al. 10.3390/rs14071730
101. Multi-Temporal Landslide Inventory-Based Statistical Susceptibility Modeling Associated With the 2017 Mw 6.5 Jiuzhaigou Earthquake, Sichuan, China L. Luo et al. 10.3389/fenvs.2022.858635
102. Literature review and bibliometric analysis on data-driven assessment of landslide susceptibility P. Lima et al. 10.1007/s11629-021-7254-9
103. Optimizing slope unit-based landslide susceptibility mapping using the priority-flood flow direction algorithm G. Yan et al. 10.1016/j.catena.2023.107657
104. Assessing the influence of temperature on slope stability in a temperate climate: A nationwide spatial probability analysis in Italy M. Loche & G. Scaringi 10.1016/j.envsoft.2024.106217
105. Seismic and environmental controls on slow-moving landslides: Insights from the 2008 Wenchuan Earthquake H. Lu et al. 10.1016/j.enggeo.2024.107865
106. Influence of landslide inventory timespan and data selection on slope unit-based susceptibility models S. Rolain et al. 10.1007/s11069-023-06092-w
107. From spatio-temporal landslide susceptibility to landslide risk forecast T. Wang et al. 10.1016/j.gsf.2023.101765
108. From scenario-based seismic hazard to scenario-based landslide hazard: fast-forwarding to the future via statistical simulations L. Lombardo & H. Tanyas 10.1007/s00477-021-02020-1
109. Exploring available landslide inventories for susceptibility analysis in Gipuzkoa province (Spain) T. Bornaetxea et al. 10.1007/s11069-023-06103-w
110. The Sustainable Development of Forest Food W. Huang et al. 10.3390/su142013092
111. A benchmark dataset and workflow for landslide susceptibility zonation M. Alvioli et al. 10.1016/j.earscirev.2024.104927
112. Seismically Induced Rockfall Hazard from Ground Motion Scenarios in Italy M. Alvioli et al. 10.2139/ssrn.4156514
113. Effect of scale and mapping unit on landslide susceptibility mapping of Mandakini River Basin, Uttarakhand, India S. Gupta & D. Shukla 10.1007/s12665-022-10487-6
114. Uncertainties of landslide susceptibility prediction: influences of different study area scales and mapping unit scales F. Huang et al. 10.1007/s40789-024-00678-w
115. The world's second-largest, recorded landslide event: Lessons learnt from the landslides triggered during and after the 2018 Mw 7.5 Papua New Guinea earthquake H. Tanyaş et al. 10.1016/j.enggeo.2021.106504
116. Mapping Susceptibility With Open-Source Tools: A New Plugin for QGIS G. Titti et al. 10.3389/feart.2022.842425
117. Landslide hazard and susceptibility maps derived from satellite and remote sensing data using limit equilibrium analysis and machine learning model B. Dashbold et al. 10.1007/s11069-022-05671-7
118. Generation of Homogeneous Slope Units Using a Novel Object-Oriented Multi-Resolution Segmentation Method Y. Li et al. 10.3390/w13233422
119. A new digital lithological map of Italy at the 1:100 000 scale for geomechanical modelling F. Bucci et al. 10.5194/essd-14-4129-2022
120. The role of morphology in the spatial distribution of short-duration rainfall extremes in Italy P. Mazzoglio et al. 10.5194/hess-26-1659-2022
121. Space-time data-driven modeling of precipitation-induced shallow landslides in South Tyrol, Italy M. Moreno et al. 10.1016/j.scitotenv.2023.169166
122. A global slope unit-based method for the near real-time prediction of earthquake-induced landslides H. Tanyas et al. 10.1016/j.geomorph.2018.10.022
123. An Improved Method for the Evaluation and Local Multi-Scale Optimization of the Automatic Extraction of Slope Units in Complex Terrains Z. Yang et al. 10.3390/rs14143444
124. Chrono-validation of near-real-time landslide susceptibility models via plug-in statistical simulations L. Lombardo & H. Tanyas 10.1016/j.enggeo.2020.105818
125. Identification of zones with high potential for biological diversity on dormant forested landslides W. Tracz et al. 10.1007/s10342-019-01170-w
126. An interpretable model for the susceptibility of rainfall-induced shallow landslides based on SHAP and XGBoost X. Zhou et al. 10.1080/10106049.2022.2076928
127. An updated landslide susceptibility model and a log-Gaussian Cox process extension for Scotland E. Bryce et al. 10.1007/s10346-024-02368-9
128. A new strategy to map landslides with a generalized convolutional neural network N. Prakash et al. 10.1038/s41598-021-89015-8
129. Landslide Susceptibility Evaluation Using Different Slope Units Based on BP Neural Network J. Huang et al. 10.1155/2022/9923775
130. Classifying Slope Unit by Combining Terrain Feature Lines Based on Digital Elevation Models H. Wang et al. 10.3390/land12010193
131. Handling high predictor dimensionality in slope-unit-based landslide susceptibility models through LASSO-penalized Generalized Linear Model D. Camilo et al. 10.1016/j.envsoft.2017.08.003
132. Application of novel ensemble models to improve landslide susceptibility mapping reliability Z. Tong et al. 10.1007/s10064-023-03328-8
133. A progressive framework combining unsupervised and optimized supervised learning for debris flow susceptibility assessment Y. Liu et al. 10.1016/j.catena.2023.107560
134. Integration of Spatial Probability and Size in Slope-Unit-Based Landslide Susceptibility Assessment: A Case Study L. Li & H. Lan 10.3390/ijerph17218055
135. A review of statistically-based landslide susceptibility models P. Reichenbach et al. 10.1016/j.earscirev.2018.03.001
136. Space-time modeling of landslide size by combining static, dynamic, and unobserved spatiotemporal factors Z. Fang et al. 10.1016/j.catena.2024.107989
137. From scenario-based seismic hazard to scenario-based landslide hazard: rewinding to the past via statistical simulations L. Luo et al. 10.1007/s00477-020-01959-x
138. Topography-driven satellite imagery analysis for landslide mapping M. Alvioli et al. 10.1080/19475705.2018.1458050
139. Landslide size matters: A new data-driven, spatial prototype L. Lombardo et al. 10.1016/j.enggeo.2021.106288
140. Advancements in Technologies and Methodologies of Machine Learning in Landslide Susceptibility Research: Current Trends and Future Directions Z. Lu et al. 10.3390/app14219639
141. A grid-based physical model to analyze the stability of slope unit S. Zhang et al. 10.1016/j.geomorph.2021.107887
142. Distinct Susceptibility Patterns of Active and Relict Landslides Reveal Distinct Triggers: A Case in Northwestern Turkey M. Loche et al. 10.3390/rs14061321
143. From ground motion simulations to landslide occurrence prediction A. Dahal et al. 10.1016/j.geomorph.2023.108898
144. SHAP-PDP hybrid interpretation of decision-making mechanism of machine learning-based landslide susceptibility mapping: A case study at Wushan District, China D. Sun et al. 10.1016/j.ejrs.2024.06.005
145. Zonation-based landslide hazard assessment using artificial neural networks in the China-Pakistan Economic Corridor Z. Jianqiang et al. 10.3389/feart.2022.927102
146. Rockfall susceptibility along Pasang Lhamu and Galchhi-Rasuwagadhi highways, Rasuwa, Central Nepal B. Pokharel et al. 10.1007/s10064-023-03174-8
147. Insights into spatial differential characteristics of landslide susceptibility from sub-region to whole-region cased by northeast Chongqing, China R. Liu et al. 10.1080/19475705.2023.2190858
148. Modelling landslide susceptibility prediction: A review and construction of semi-supervised imbalanced theory F. Huang et al. 10.1016/j.earscirev.2024.104700
149. On the prediction of landslide occurrences and sizes via Hierarchical Neural Networks Q. Aguilera et al. 10.1007/s00477-022-02215-0
150. Dynamic rainfall-induced landslide susceptibility: A step towards a unified forecasting system M. Ahmed et al. 10.1016/j.jag.2023.103593
151. KNN-GCN: A Deep Learning Approach for Slope-Unit-Based Landslide Susceptibility Mapping Incorporating Spatial Correlations D. Xia et al. 10.1007/s11004-023-10132-3
152. Global Landslide Finder: Detecting the Time and Place of Landslides with Dense Earth Observation Time Series M. Aufaristama et al. 10.3390/geohazards5030039
153. Characterization and quantification of path dependency in landslide susceptibility J. Samia et al. 10.1016/j.geomorph.2017.04.039
154. A new slope unit extraction method based on improved marked watershed L. Cheng et al. 10.1051/matecconf/201823204070
155. Slope Unit Maker (SUMak): an efficient and parameter-free algorithm for delineating slope units to improve landslide modeling J. Woodard et al. 10.5194/nhess-24-1-2024
156. A new slope unit extraction method based on terrain topology searching and vector similarity constraint for landslide analysis L. Zhang et al. 10.1016/j.catena.2024.108355
157. Quantitatively detecting ground surface changes of slope failure caused by heavy rain using ALOS-2/PALSAR-2 data: a case study in Japan X. Wang et al. 10.1038/s41598-024-73372-1
158. Landslide mapping using object-based image analysis and open source tools P. Amatya et al. 10.1016/j.enggeo.2021.106000
159. A novel image segmentation method based on spatial autocorrelation identifies A-type potassium channel clusters in the thalamus C. Dávid et al. 10.7554/eLife.89361.3
160. Accounting for covariate distributions in slope-unit-based landslide susceptibility models. A case study in the alpine environment G. Amato et al. 10.1016/j.enggeo.2019.105237
161. Integration of Remotely Sensed Soil Sealing Data in Landslide Susceptibility Mapping T. Luti et al. 10.3390/rs12091486
162. The future of landslides’ past—a framework for assessing consecutive landsliding systems A. Temme et al. 10.1007/s10346-020-01405-7
163. Surface temperature controls the pattern of post-earthquake landslide activity M. Loche et al. 10.1038/s41598-022-04992-8
164. Automatic Extraction for Land Parcels Based on Multi-Scale Segmentation F. Liu et al. 10.3390/land13020158
165. Dynamic path-dependent landslide susceptibility modelling J. Samia et al. 10.5194/nhess-20-271-2020
166. Landslide susceptibility mapping along the upper Jinsha River, south-western China: a comparison of hydrological and curvature watershed methods for slope unit classification X. Sun et al. 10.1007/s10064-020-01849-0
167. Geomorphological slope units of the Himalayas M. Alvioli et al. 10.1080/17445647.2022.2052768
168. Application of different watershed units to debris flow susceptibility mapping: A case study of Northeast China J. Lv et al. 10.3389/feart.2023.1118160
169. Combining Satellite InSAR, Slope Units and Finite Element Modeling for Stability Analysis in Mining Waste Disposal Areas J. López-Vinielles et al. 10.3390/rs13102008
170. Point process-based modeling of multiple debris flow landslides using INLA: an application to the 2009 Messina disaster L. Lombardo et al. 10.1007/s00477-018-1518-0
171. Unified landslide hazard assessment using hurdle models: a case study in the Island of Dominica E. Bryce et al. 10.1007/s00477-022-02239-6
172. 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
173. An Automatic Method for Delimiting Deformation Area in InSAR Based on HNSW-DBSCAN Clustering Algorithm J. Han et al. 10.3390/rs15174287
174. Optimizing landslide susceptibility zonation: Effects of DEM spatial resolution and slope unit delineation on logistic regression models R. Schlögel et al. 10.1016/j.geomorph.2017.10.018
175. Space-time landslide predictive modelling L. Lombardo et al. 10.1016/j.earscirev.2020.103318
176. The propagation of inventory-based positional errors into statistical landslide susceptibility models S. Steger et al. 10.5194/nhess-16-2729-2016
177. Investigating earthquake legacy effect on hillslope deformation using InSAR‐derived time series K. He et al. 10.1002/esp.5746
178. A closer look into variables controlling hillslope deformations in the Three Gorges Reservoir Area H. Sang et al. 10.1016/j.enggeo.2024.107584
179. Modeling the area of co-seismic landslides via data-driven models: The Kaikōura example M. Moreno et al. 10.1016/j.enggeo.2023.107121
180. LAND-SE: a software for statistically based landslide susceptibility zonation, version 1.0 M. Rossi & P. Reichenbach 10.5194/gmd-9-3533-2016
181. Innovative landslide susceptibility mapping supported by geomorphon and geographical detector methods W. Luo & C. Liu 10.1007/s10346-017-0893-9
182. Counteracting flawed landslide data in statistically based landslide susceptibility modelling for very large areas: a national-scale assessment for Austria P. Lima et al. 10.1007/s10346-021-01693-7
183. Spatial clustering and modelling for landslide susceptibility mapping in the north of the Kathmandu Valley, Nepal B. Pokharel et al. 10.1007/s10346-020-01558-5
184. Assessment of earthquake-induced landslide inventories and susceptibility maps using slope unit-based logistic regression and geospatial statistics B. Pokharel et al. 10.1038/s41598-021-00780-y