45 citations as recorded by crossref.

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2. Landslide Mapping in Vegetated Areas Using Change Detection Based on Optical and Polarimetric SAR Data S. Plank et al. 10.3390/rs8040307
3. Landslide Detection Using Residual Networks and the Fusion of Spectral and Topographic Information M. Sameen & B. Pradhan 10.1109/ACCESS.2019.2935761
4. Landslide Mapping and Characterization through Infrared Thermography (IRT): Suggestions for a Methodological Approach from Some Case Studies W. Frodella et al. 10.3390/rs9121281
5. Web data mining for automatic inventory of geohazards at national scale A. Battistini et al. 10.1016/j.apgeog.2013.06.012
6. A hybrid model to overcome landslide inventory incompleteness issue for landslide susceptibility prediction J. Tan et al. 10.1080/10106049.2024.2322066
7. Mapping of shallow landslides with object-based image analysis from unmanned aerial vehicle data R. Comert et al. 10.1016/j.enggeo.2019.105264
8. Exploring a landslide inventory created by automated web data mining: the case of Italy R. Franceschini et al. 10.1007/s10346-021-01799-y
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10. Remote Sensing for Landslide Investigations: An Overview of Recent Achievements and Perspectives M. Scaioni et al. 10.3390/rs6109600
11. Landslide Detection Mapping Employing CNN, ResNet, and DenseNet in the Three Gorges Reservoir, China T. Liu et al. 10.1109/JSTARS.2021.3117975
12. Review article: the use of remotely piloted aircraft systems (RPASs) for natural hazards monitoring and management D. Giordan et al. 10.5194/nhess-18-1079-2018
13. Inventory of landslides triggered by an extreme rainfall event in Marche-Umbria, Italy, on 15 September 2022 M. Santangelo et al. 10.1038/s41597-023-02336-3
14. Inventory of shallow landslides triggered by extreme precipitation in July 2023 in Beijing, China H. Ma & F. Wang 10.1038/s41597-024-03901-0
15. A Deep-Learning-Based Algorithm for Landslide Detection over Wide Areas Using InSAR Images Considering Topographic Features N. Li et al. 10.3390/s24144583
16. Recognition of large scale deep-seated landslides in forest areas of Taiwan using high resolution topography C. Lin et al. 10.1016/j.jseaes.2012.10.022
17. Data Fusion Technique Using Wavelet Transform and Taguchi Methods for Automatic Landslide Detection From Airborne Laser Scanning Data and QuickBird Satellite Imagery B. Pradhan et al. 10.1109/TGRS.2015.2484325
18. Generating an optimal DTM from airborne laser scanning data for landslide mapping in a tropical forest environment K. Razak et al. 10.1016/j.geomorph.2013.02.021
19. Évaluation de l’aléa glissements de terrain : état de l’art et perspectives pour la cartographie réglementaire en France Y. Thiery & M. Terrier 10.1051/geotech/2019003
20. AI-Based Susceptibility Analysis of Shallow Landslides Induced by Heavy Rainfall in Tianshui, China T. Qi et al. 10.3390/rs13091819
21. Rapid onset hazards, fault-controlled landslides and multi-method emergency decision-making F. Guo et al. 10.1007/s11629-021-6941-x
22. Seasonal landslide mapping and estimation of landslide mobilization rates using aerial and satellite images F. Fiorucci et al. 10.1016/j.geomorph.2011.01.013
23. 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
24. Landslide-prone area retrieval and earthquake-inducing hazard probability assessment based on InSAR analysis L. Zou et al. 10.1007/s10346-023-02079-7
25. Automatic identification of active landslides over wide areas from time-series InSAR measurements using Faster RCNN J. Cai et al. 10.1016/j.jag.2023.103516
26. Landslide mapping with remote sensing: challenges and opportunities C. Zhong et al. 10.1080/01431161.2019.1672904
27. An approach to reduce mapping errors in the production of landslide inventory maps M. Santangelo et al. 10.5194/nhess-15-2111-2015
28. Identification and Analysis of the Geohazards Located in an Alpine Valley Based on Multi-Source Remote Sensing Data Y. Yang et al. 10.3390/s24134057
29. Developmental characteristics of rainfall-induced landslides from 1999 to 2016 in Wenzhou City of China H. Qin et al. 10.3389/feart.2022.1005199
30. Integrating Expert Knowledge with Statistical Analysis for Landslide Susceptibility Assessment at Regional Scale C. Chalkias et al. 10.3390/geosciences6010014
31. Exploring event landslide mapping using Sentinel-1 SAR backscatter products M. Santangelo et al. 10.1016/j.geomorph.2021.108021
32. Detection and analysis of landslide geomorphology based on UAV vertical photogrammetry R. Bi et al. 10.1007/s11629-022-7766-y
33. Landslide inventory map of the upper Sinni River valley, Southern Italy M. Santangelo et al. 10.1080/17445647.2014.949313
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35. Evaluating survey instruments and methods in a steep channel D. Scott et al. 10.1016/j.geomorph.2016.08.020
36. Spatial prediction of landslide-susceptible zones in El-Qaá area, Egypt, using an integrated approach based on GIS statistical analysis S. Abuzied & H. Alrefaee 10.1007/s10064-018-1302-x
37. r.survey: a tool for calculating visibility of variable-size objects based on orientation T. Bornaetxea & I. Marchesini 10.1080/13658816.2021.1942476
38. Impact of event landslides on road networks: a statistical analysis of two Italian case studies M. Donnini et al. 10.1007/s10346-017-0829-4
39. Criteria for the optimal selection of remote sensing optical images to map event landslides F. Fiorucci et al. 10.5194/nhess-18-405-2018
40. Pixel-based classification method for earthquake-induced landslide mapping using remotely sensed imagery, geospatial data and temporal change information A. Asadi et al. 10.1007/s11069-023-06399-8
41. Landslide inventory maps: New tools for an old problem F. Guzzetti et al. 10.1016/j.earscirev.2012.02.001
42. Geohazard Recognition and Inventory Mapping Using Airborne LiDAR Data in Complex Mountainous Areas C. Guo et al. 10.1007/s12583-021-1467-2
43. Preface: Landslide–transport network interactions F. Taylor et al. 10.5194/nhess-20-2585-2020
44. Effectiveness of visually analyzing LiDAR DTM derivatives for earth and debris slide inventory mapping for statistical susceptibility modeling H. Petschko et al. 10.1007/s10346-015-0622-1
45. The Use of High-Resolution Remote Sensing Data in Preparation of Input Data for Large-Scale Landslide Hazard Assessments M. Sinčić et al. 10.3390/land11081360