79 citations as recorded by crossref.

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3. Detection of slow‐moving landslides through automated monitoring of surface deformation using Sentinel‐2 satellite imagery M. Van Wyk de Vries et al. https://doi.org/10.1002/esp.5775
4. Cost-effective disaster-induced land cover analysis: a semi-automatic methodology Using machine learning and satellite imagery M. I. Volke et al. https://doi.org/10.1080/01431161.2023.2292015
5. Initiation mechanisms for multiple post-fire debris flow events: insights from the 2021 Yaoyao Fire in Western Sichuan, China K. He et al. https://doi.org/10.1007/s10346-025-02553-4
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7. The The Evolution of Remote Sensing for Regional Characterization and Response to Geohazards and Extreme Events S. Anderson https://doi.org/10.64862/ajeg.2025.2sp.59.177
8. Semi-automatic mapping of shallow landslides using free Sentinel-2 images and Google Earth Engine D. Notti et al. https://doi.org/10.5194/nhess-23-2625-2023
9. Review of landslide inventories for Nepal between 2010 and 2021 reveals data gaps in global landslide hotspot E. Harvey et al. https://doi.org/10.1007/s11069-024-07013-1
10. Mapping landslides from space: A review A. Novellino et al. https://doi.org/10.1007/s10346-024-02215-x
11. Canopy height Mapper: A google earth engine application for predicting global canopy heights combining GEDI with multi-source data C. Alvites et al. https://doi.org/10.1016/j.envsoft.2024.106268
12. Unraveling the causes and impacts of increasing flood disasters in the kathmandu valley: Lessons from the unprecedented September 2024 floods K. Lamichhane et al. https://doi.org/10.1016/j.nhres.2025.04.001
13. Responses to Landslides and Landslide Mapping on the Blue Ridge Escarpment, Polk County, North Carolina, USA R. Wooten et al. https://doi.org/10.2113/EEG-D-21-00022
14. Identifying recurrent and persistent landslides using satellite imagery and deep learning: A 30-year analysis of the Himalaya T. Chen et al. https://doi.org/10.1016/j.scitotenv.2024.171161
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16. Evaluating the performance of random forest, support vector machine, gradient tree boost, and CART for improved crop-type monitoring using greenest pixel composite in Google Earth Engine C. Savitha & R. Talari https://doi.org/10.1007/s10661-025-13880-3
17. Sentinel-1 SAR-based globally distributed co-seismic landslide detection by deep neural networks L. Nava et al. https://doi.org/10.5194/gmd-19-167-2026
18. A Google Earth Engine Platform to Integrate Multi-Satellite and Citizen Science Data for the Monitoring of River Ice Dynamics M. Abdelkader et al. https://doi.org/10.3390/rs16081368
19. Multi-temporal landslide inventory mapping after wildfire and implications for post-fire debris flow activity R. Zhou et al. https://doi.org/10.1016/j.enggeo.2025.107948
20. Hillslope torrential hazard cascades in tropical mountains M. Arango-Carmona et al. https://doi.org/10.5194/nhess-25-3641-2025
21. INASTER (Indonesia Disaster Platform): Cloud Computing Based Geospatial Platform in Monitoring Indonesia Natural Disaster N. Jouhary et al. https://doi.org/10.1088/1755-1315/1569/1/012010
22. Detecting Coseismic Landslides in GEE Using Machine Learning Algorithms on Combined Optical and Radar Imagery S. Peters et al. https://doi.org/10.3390/rs16101722
23. Evaluation of machine learning-based algorithms for landslide detection across satellite sensors for the 2019 Cyclone Idai event, Chimanimani District, Zimbabwe R. Das & K. Wegmann https://doi.org/10.1007/s10346-022-01912-9
24. Enhancing FAIR Data Services in Agricultural Disaster: A Review L. Hu et al. https://doi.org/10.3390/rs15082024
25. Event-based mapping and spatial pattern analysis of landslides in parts of central Vietnam R. Das et al. https://doi.org/10.1007/s11069-025-07670-w
26. Modelling of large wood export at a watershed scale D. Komori et al. https://doi.org/10.1002/esp.5282
27. Multi-Temporal Satellite Image Composites in Google Earth Engine for Improved Landslide Visibility: A Case Study of a Glacial Landscape E. Lindsay et al. https://doi.org/10.3390/rs14102301
28. Assessing hydrological erosion estimation using the Revised Universal Soil Loss Equation (RUSLE) model in Google Earth Engine: a case study of Medjerda River Catchment, Tunisia M. Cherif et al. https://doi.org/10.1007/s41207-025-00767-5
29. A service-oriented collaborative approach to disaster decision support by integrating geospatial resources and task chain Z. Fang et al. https://doi.org/10.1016/j.jag.2023.103217
30. Mapping land-use and land-cover changes through the integration of satellite and airborne remote sensing data M. Lin et al. https://doi.org/10.1007/s10661-024-12424-5
31. Detailed inventory and initial analysis of landslides triggered by extreme rainfall in the northern Huaiji County, Guangdong Province, China, from June 6 to 9, 2020 C. Xie et al. https://doi.org/10.1186/s40677-025-00311-1
32. Effectiveness evaluation of combining SAR and multiple optical data on land cover mapping of a fragmented landscape in a cloud computing platform G. Romano et al. https://doi.org/10.3389/frsen.2025.1535418
33. Exploration of Multi-Decadal Landslide Frequency and Vegetation Recovery Conditions Using Remote-Sensing Big Data M. Aman et al. https://doi.org/10.1007/s41748-024-00432-x
34. Evaluating root strength index as an indicator of landslide-prone slopes in eastern kentucky M. Swallom et al. https://doi.org/10.1007/s10346-024-02384-9
35. 2021 Turkey mega forest Fires: Biodiversity measurements of the IUCN Red List wildlife mammals in Sentinel-2 based burned areas F. Aydin-Kandemir & N. Demir https://doi.org/10.1016/j.asr.2023.01.031
36. On the emergence of geospatial cloud-based platforms for disaster risk management: A global scientometric review of google earth engine applications M. Waleed & M. Sajjad https://doi.org/10.1016/j.ijdrr.2023.104056
37. The Relationship between Large Wood Export and the Long-Term Large Wood Budget on an Annual Scale in Japan, Using Storage Function with the Lumped Hydrological Method Y. Abe et al. https://doi.org/10.3390/w16070920
38. Testing NDVI and U-Net for automated mapping of multiple-occurrence regional landslide events using satellite and aerial multispectral data (Casola Valsenio, Emilia-Romagna, Northern Apennines, Italy) M. Berti et al. https://doi.org/10.1007/s10346-025-02671-z
39. Multi-field Coupling Early Warning of Climate-induced Cascading Landslide Hazards: The Likan Landslides in the Upper Yellow River, China Z. Gu et al. https://doi.org/10.1016/j.jag.2026.105198
40. Landslide susceptibility and building exposure assessment using machine learning models and geospatial analysis techniques C. Luu et al. https://doi.org/10.1016/j.asr.2024.08.046
41. Capturing the complete landslide–debris-rich flood continuum for accurate inventory, susceptibility and exposure mapping – lessons from Cyclone Idai A. Dille et al. https://doi.org/10.5194/nhess-26-2561-2026
42. Rapid Landslide Detection Following an Extreme Rainfall Event Using Remote Sensing Indices, Synthetic Aperture Radar Imagery, and Probabilistic Methods A. Chrysafi et al. https://doi.org/10.3390/land14010021
43. WITHDRAWN: Inventory and distribution patterns of debris flow gullies in the Lhasa-Linzhi section of Sichuan-Tibet Railway G. Hu et al. https://doi.org/10.1016/j.nhres.2024.01.003
44. Extreme Mediterranean rainfall impact on sedimentary routing systems: what can we learn from Storm Alex using in situ detrital 10Be? A. Mariotti et al. https://doi.org/10.1016/j.geomorph.2025.110061
45. Event-based rainfall-induced landslide inventories and rainfall thresholds for Malawi P. Niyokwiringirwa et al. https://doi.org/10.1007/s10346-023-02203-7
46. Expansion and Evolution of a Typical Resource-Based Mining City in Transition Using the Google Earth Engine: A Case Study of Datong, China M. Xue et al. https://doi.org/10.3390/rs13204045
47. A semi-supervised multi-temporal landslide and flash flood event detection methodology for unexplored regions using massive satellite image time series A. Deijns et al. https://doi.org/10.1016/j.isprsjprs.2024.07.010
48. Geomorphic imprint of high-mountain floods: insights from the 2022 hydrological extreme across the upper Indus River catchment in the northwestern Himalayas A. Kashyap et al. https://doi.org/10.5194/esurf-13-147-2025
49. Learnings from rapid response efforts to remotely detect landslides triggered by the August 2021 Nippes earthquake and Tropical Storm Grace in Haiti P. Amatya et al. https://doi.org/10.1007/s11069-023-06096-6
50. Assessing torrentiality in catchments of the tropical Andes: A morphometric approach S. Machuca et al. https://doi.org/10.1016/j.jsames.2023.104775
51. Geomorphometry and terrain analysis: data, methods, platforms and applications L. Xiong et al. https://doi.org/10.1016/j.earscirev.2022.104191
52. A Survey of Emergencies Management Systems in Smart Cities D. Costa et al. https://doi.org/10.1109/ACCESS.2022.3180033
53. Combination of optical images and SAR images for detecting landslide scars, using a classification and regression tree S. Phakdimek et al. https://doi.org/10.1080/01431161.2023.2224096
54. Automated determination of landslide locations after large trigger events: advantages and disadvantages compared to manual mapping D. Milledge et al. https://doi.org/10.5194/nhess-22-481-2022
55. Landslides Triggered by Medicane Ianos in Greece, September 2020: Rapid Satellite Mapping and Field Survey S. Valkaniotis et al. https://doi.org/10.3390/app122312443
56. Inventories of natural hazards in under-reported regions: a multi-method insight from a tropical mountainous landscape V. Kanyiginya et al. https://doi.org/10.1080/19376812.2023.2280589
57. Exploring the Potential of the Google Earth Engine (GEE) Platform for Analysing Forest Disturbance Patterns with Big Data T. Çinar & A. Aydin https://doi.org/10.15446/esrj.v27n4.110128
58. Insights on the growth and mobility of debris flows from repeat high-resolution lidar C. Scheip & K. Wegmann https://doi.org/10.1007/s10346-022-01862-2
59. Augmentation of WRF-Hydro to simulate overland-flow- and streamflow-generated debris flow susceptibility in burn scars C. Li et al. https://doi.org/10.5194/nhess-22-2317-2022
60. Detection of landslide timing, reactivation and precursory motion during the 2018, Lombok, Indonesia earthquake sequence with Sentinel-1 K. Burrows et al. https://doi.org/10.5194/esurf-13-1039-2025
61. Earthquake-triggered landslides and Environmental Seismic Intensity: insights from the 2018 Papua New Guinea earthquake (M w 7.5) A. Sridharan et al. https://doi.org/10.1080/27669645.2023.2233140
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63. Landslides Detection and Mapping with an Advanced Multi-Temporal Satellite Optical Technique V. Satriano et al. https://doi.org/10.3390/rs15030683
64. Hybrid pixel-based and object-based image analysis approach for landslides rapid mapping: the extreme rainfall in Emilia-Romagna (Italy) May 2023 case study F. Filipponi et al. https://doi.org/10.1007/s11069-025-07705-2
65. Change detection-based co-seismic landslide mapping through extended morphological profiles and ensemble strategy X. Wang et al. https://doi.org/10.1016/j.isprsjprs.2022.03.011
66. Spatial patterns and influencing factors of debris flows in the middle Yarlung Zangbo River on the Tibetan Plateau G. Hu et al. https://doi.org/10.1007/s10064-025-04203-4
67. Earthquake-induced soil landslides: volume estimates and uncertainties with the existing scaling exponents A. Yunus et al. https://doi.org/10.1038/s41598-023-35088-6
68. Global Landslide Finder: Detecting the Time and Place of Landslides with Dense Earth Observation Time Series M. Aufaristama et al. https://doi.org/10.3390/geohazards5030039
69. Mass Movements in Wetlands: An Analysis of a Typical Amazon Delta-Estuary Environment A. de Lima et al. https://doi.org/10.3390/geohazards6030040
70. Combining remote sensing techniques and field surveys for post-earthquake reconnaissance missions G. Giardina et al. https://doi.org/10.1007/s10518-023-01716-9
71. Accelerated Adoption of Google Earth Engine for Mangrove Monitoring: A Global Review K. Islam et al. https://doi.org/10.3390/rs17132290
72. Monitoring volcanic gas hazards in Goma DRC using GIS and Google Earth Engine H. Abdelhamid et al. https://doi.org/10.1007/s44288-025-00189-4
73. Spatial distribution characteristics of climate-induced landslides in the Eastern Himalayas D. Uwizeyimana et al. https://doi.org/10.1007/s11629-024-8869-4
74. Automated unsupervised landslide detection in infrastructure-exposed mountainous regions using Sentinel-2 NDVI time-series analysis X. Wang et al. https://doi.org/10.1016/j.autcon.2026.106967
75. Ten simple rules for researchers who want to develop web apps S. Saia et al. https://doi.org/10.1371/journal.pcbi.1009663
76. ML-CASCADE: A machine learning and cloud computing-based tool for rapid and automated mapping of landslides using earth observation data N. Sharma & M. Saharia https://doi.org/10.1007/s10346-024-02360-3
77. The role of wildfires and forest harvesting on geohazards and channel instability during the November 2021 atmospheric river in southwestern British Columbia, Canada C. Hancock & K. Wlodarczyk https://doi.org/10.1002/esp.6065
78. Detection of Flash Flood Inundated Areas Using Relative Difference in NDVI from Sentinel-2 Images: A Case Study of the August 2020 Event in Charikar, Afghanistan M. Atefi & H. Miura https://doi.org/10.3390/rs14153647
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