Articles | Volume 21, issue 5
https://doi.org/10.5194/nhess-21-1495-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-21-1495-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 May 2021
Research article |
| 12 May 2021
| 12 May 2021
HazMapper: a global open-source natural hazard mapping application in Google Earth Engine
Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University, Raleigh, NC 27695, USA
North Carolina Geological Survey, Division of Energy, Mineral, and Land Resources, Department of Environmental Quality, Swannanoa, NC 28778, USA
Karl W. Wegmann
Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University, Raleigh, NC 27695, USA
Center for Geospatial Analytics, North Carolina State University, Raleigh, NC 27695, USA
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- Detection of slow‐moving landslides through automated monitoring of surface deformation using Sentinel‐2 satellite imagery M. Van Wyk de Vries et al. 10.1002/esp.5775
- Cost-effective disaster-induced land cover analysis: a semi-automatic methodology Using machine learning and satellite imagery M. I. Volke et al. 10.1080/01431161.2023.2292015
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- Canopy height Mapper: A google earth engine application for predicting global canopy heights combining GEDI with multi-source data C. Alvites et al. 10.1016/j.envsoft.2024.106268
- Unraveling the Causes and Impacts of Increasing Flood Disasters in the Kathmandu Valley: Lessons from the Unprecedented September 2024 Floods K. Lamichhane et al. 10.1016/j.nhres.2025.04.001
- Responses to Landslides and Landslide Mapping on the Blue Ridge Escarpment, Polk County, North Carolina, USA R. Wooten et al. 10.2113/EEG-D-21-00022
- Identifying recurrent and persistent landslides using satellite imagery and deep learning: A 30-year analysis of the Himalaya T. Chen et al. 10.1016/j.scitotenv.2024.171161
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- A Google Earth Engine Platform to Integrate Multi-Satellite and Citizen Science Data for the Monitoring of River Ice Dynamics M. Abdelkader et al. 10.3390/rs16081368
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- A service-oriented collaborative approach to disaster decision support by integrating geospatial resources and task chain Z. Fang et al. 10.1016/j.jag.2023.103217
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- 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. 10.1186/s40677-025-00311-1
- 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. 10.3389/frsen.2025.1535418
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- Landslide susceptibility and building exposure assessment using machine learning models and geospatial analysis techniques C. Luu et al. 10.1016/j.asr.2024.08.046
- Rapid Landslide Detection Following an Extreme Rainfall Event Using Remote Sensing Indices, Synthetic Aperture Radar Imagery, and Probabilistic Methods A. Chrysafi et al. 10.3390/land14010021
- WITHDRAWN: Inventory and distribution patterns of debris flow gullies in the Lhasa-Linzhi section of Sichuan-Tibet Railway G. Hu et al. 10.1016/j.nhres.2024.01.003
- Event-based rainfall-induced landslide inventories and rainfall thresholds for Malawi P. Niyokwiringirwa et al. 10.1007/s10346-023-02203-7
- 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. 10.3390/rs13204045
- 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. 10.1016/j.isprsjprs.2024.07.010
- 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. 10.5194/esurf-13-147-2025
- 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. 10.1007/s11069-023-06096-6
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- A Survey of Emergencies Management Systems in Smart Cities D. Costa et al. 10.1109/ACCESS.2022.3180033
- Combination of optical images and SAR images for detecting landslide scars, using a classification and regression tree S. Phakdimek et al. 10.1080/01431161.2023.2224096
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- Inventories of natural hazards in under-reported regions: a multi-method insight from a tropical mountainous landscape V. Kanyiginya et al. 10.1080/19376812.2023.2280589
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- Insights on the growth and mobility of debris flows from repeat high-resolution lidar C. Scheip & K. Wegmann 10.1007/s10346-022-01862-2
- Augmentation of WRF-Hydro to simulate overland-flow- and streamflow-generated debris flow susceptibility in burn scars C. Li et al. 10.5194/nhess-22-2317-2022
- Earthquake-triggered landslides and Environmental Seismic Intensity: insights from the 2018 Papua New Guinea earthquake (M w 7.5) A. Sridharan et al. 10.1080/27669645.2023.2233140
- Cloud-based interactive susceptibility modeling of gully erosion in Google Earth Engine G. Titti et al. 10.1016/j.jag.2022.103089
- Landslides Detection and Mapping with an Advanced Multi-Temporal Satellite Optical Technique V. Satriano et al. 10.3390/rs15030683
- Change detection-based co-seismic landslide mapping through extended morphological profiles and ensemble strategy X. Wang et al. 10.1016/j.isprsjprs.2022.03.011
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- Earthquake-induced soil landslides: volume estimates and uncertainties with the existing scaling exponents A. Yunus et al. 10.1038/s41598-023-35088-6
- Global Landslide Finder: Detecting the Time and Place of Landslides with Dense Earth Observation Time Series M. Aufaristama et al. 10.3390/geohazards5030039
- Combining remote sensing techniques and field surveys for post-earthquake reconnaissance missions G. Giardina et al. 10.1007/s10518-023-01716-9
- Spatial distribution characteristics of climate-induced landslides in the Eastern Himalayas D. Uwizeyimana et al. 10.1007/s11629-024-8869-4
- Ten simple rules for researchers who want to develop web apps S. Saia et al. 10.1371/journal.pcbi.1009663
- 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 10.1007/s10346-024-02360-3
- 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 10.1002/esp.6065
- 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 10.3390/rs14153647
- A Batch Pixel-Based Algorithm to Composite Landsat Time Series Images J. Li et al. 10.3390/rs14174252
- Earth Observation for Sustainable Infrastructure: A Review Y. Song & P. Wu 10.3390/rs13081528
- Spatiotemporal variations of fatal landslides in Turkey T. Görüm & S. Fidan 10.1007/s10346-020-01580-7
64 citations as recorded by crossref.
- ECMWF’s societal impact through service provision, partnerships and collaborations F. Venuti et al. 10.1016/j.jemets.2025.100013
- Generating landslide density heatmaps for rapid detection using open-access satellite radar data in Google Earth Engine A. Handwerger et al. 10.5194/nhess-22-753-2022
- Detection of slow‐moving landslides through automated monitoring of surface deformation using Sentinel‐2 satellite imagery M. Van Wyk de Vries et al. 10.1002/esp.5775
- Cost-effective disaster-induced land cover analysis: a semi-automatic methodology Using machine learning and satellite imagery M. I. Volke et al. 10.1080/01431161.2023.2292015
- Initiation mechanisms for multiple post-fire debris flow events: insights from the 2021 Yaoyao Fire in Western Sichuan, China K. He et al. 10.1007/s10346-025-02553-4
- Managing natural disasters: An analysis of technological advancements, opportunities, and challenges M. Krichen et al. 10.1016/j.iotcps.2023.09.002
- Semi-automatic mapping of shallow landslides using free Sentinel-2 images and Google Earth Engine D. Notti et al. 10.5194/nhess-23-2625-2023
- Review of landslide inventories for Nepal between 2010 and 2021 reveals data gaps in global landslide hotspot E. Harvey et al. 10.1007/s11069-024-07013-1
- Mapping landslides from space: A review A. Novellino et al. 10.1007/s10346-024-02215-x
- Canopy height Mapper: A google earth engine application for predicting global canopy heights combining GEDI with multi-source data C. Alvites et al. 10.1016/j.envsoft.2024.106268
- Unraveling the Causes and Impacts of Increasing Flood Disasters in the Kathmandu Valley: Lessons from the Unprecedented September 2024 Floods K. Lamichhane et al. 10.1016/j.nhres.2025.04.001
- Responses to Landslides and Landslide Mapping on the Blue Ridge Escarpment, Polk County, North Carolina, USA R. Wooten et al. 10.2113/EEG-D-21-00022
- Identifying recurrent and persistent landslides using satellite imagery and deep learning: A 30-year analysis of the Himalaya T. Chen et al. 10.1016/j.scitotenv.2024.171161
- Debris‐Flood Hazard Assessments in Steep Streams M. Jakob et al. 10.1029/2021WR030907
- 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 10.1007/s10661-025-13880-3
- A Google Earth Engine Platform to Integrate Multi-Satellite and Citizen Science Data for the Monitoring of River Ice Dynamics M. Abdelkader et al. 10.3390/rs16081368
- Multi-temporal landslide inventory mapping after wildfire and implications for post-fire debris flow activity R. Zhou et al. 10.1016/j.enggeo.2025.107948
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- 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 10.1007/s10346-022-01912-9
- Enhancing FAIR Data Services in Agricultural Disaster: A Review L. Hu et al. 10.3390/rs15082024
- Modelling of large wood export at a watershed scale D. Komori et al. 10.1002/esp.5282
- Multi-Temporal Satellite Image Composites in Google Earth Engine for Improved Landslide Visibility: A Case Study of a Glacial Landscape E. Lindsay et al. 10.3390/rs14102301
- 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. 10.1007/s41207-025-00767-5
- A service-oriented collaborative approach to disaster decision support by integrating geospatial resources and task chain Z. Fang et al. 10.1016/j.jag.2023.103217
- Mapping land-use and land-cover changes through the integration of satellite and airborne remote sensing data M. Lin et al. 10.1007/s10661-024-12424-5
- 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. 10.1186/s40677-025-00311-1
- 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. 10.3389/frsen.2025.1535418
- Exploration of Multi-Decadal Landslide Frequency and Vegetation Recovery Conditions Using Remote-Sensing Big Data M. Aman et al. 10.1007/s41748-024-00432-x
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- Landslide susceptibility and building exposure assessment using machine learning models and geospatial analysis techniques C. Luu et al. 10.1016/j.asr.2024.08.046
- Rapid Landslide Detection Following an Extreme Rainfall Event Using Remote Sensing Indices, Synthetic Aperture Radar Imagery, and Probabilistic Methods A. Chrysafi et al. 10.3390/land14010021
- WITHDRAWN: Inventory and distribution patterns of debris flow gullies in the Lhasa-Linzhi section of Sichuan-Tibet Railway G. Hu et al. 10.1016/j.nhres.2024.01.003
- Event-based rainfall-induced landslide inventories and rainfall thresholds for Malawi P. Niyokwiringirwa et al. 10.1007/s10346-023-02203-7
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- 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. 10.1016/j.isprsjprs.2024.07.010
- 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. 10.5194/esurf-13-147-2025
- 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. 10.1007/s11069-023-06096-6
- Assessing torrentiality in catchments of the tropical Andes: A morphometric approach S. Machuca et al. 10.1016/j.jsames.2023.104775
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- Landslides Triggered by Medicane Ianos in Greece, September 2020: Rapid Satellite Mapping and Field Survey S. Valkaniotis et al. 10.3390/app122312443
- Inventories of natural hazards in under-reported regions: a multi-method insight from a tropical mountainous landscape V. Kanyiginya et al. 10.1080/19376812.2023.2280589
- Exploring the Potential of the Google Earth Engine (GEE) Platform for Analysing Forest Disturbance Patterns with Big Data T. Çinar & A. Aydin 10.15446/esrj.v27n4.110128
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- Augmentation of WRF-Hydro to simulate overland-flow- and streamflow-generated debris flow susceptibility in burn scars C. Li et al. 10.5194/nhess-22-2317-2022
- Earthquake-triggered landslides and Environmental Seismic Intensity: insights from the 2018 Papua New Guinea earthquake (M w 7.5) A. Sridharan et al. 10.1080/27669645.2023.2233140
- Cloud-based interactive susceptibility modeling of gully erosion in Google Earth Engine G. Titti et al. 10.1016/j.jag.2022.103089
- Landslides Detection and Mapping with an Advanced Multi-Temporal Satellite Optical Technique V. Satriano et al. 10.3390/rs15030683
- Change detection-based co-seismic landslide mapping through extended morphological profiles and ensemble strategy X. Wang et al. 10.1016/j.isprsjprs.2022.03.011
- Spatial patterns and influencing factors of debris flows in the middle Yarlung Zangbo River on the Tibetan Plateau G. Hu et al. 10.1007/s10064-025-04203-4
- Earthquake-induced soil landslides: volume estimates and uncertainties with the existing scaling exponents A. Yunus et al. 10.1038/s41598-023-35088-6
- Global Landslide Finder: Detecting the Time and Place of Landslides with Dense Earth Observation Time Series M. Aufaristama et al. 10.3390/geohazards5030039
- Combining remote sensing techniques and field surveys for post-earthquake reconnaissance missions G. Giardina et al. 10.1007/s10518-023-01716-9
- Spatial distribution characteristics of climate-induced landslides in the Eastern Himalayas D. Uwizeyimana et al. 10.1007/s11629-024-8869-4
- Ten simple rules for researchers who want to develop web apps S. Saia et al. 10.1371/journal.pcbi.1009663
- 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 10.1007/s10346-024-02360-3
- 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 10.1002/esp.6065
- 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 10.3390/rs14153647
- A Batch Pixel-Based Algorithm to Composite Landsat Time Series Images J. Li et al. 10.3390/rs14174252
Latest update: 30 Jun 2025
Short summary
For many decades, natural disasters have been monitored by trained analysts using multiple satellite images to observe landscape change. This approach is incredibly useful, but our new tool, HazMapper, offers researchers and the scientifically curious public a web-accessible
cloud-based tool to perform similar analysis. We intend for the tool to both be used in scientific research and provide rapid response to global natural disasters like landslides, wildfires, and volcanic eruptions.
For many decades, natural disasters have been monitored by trained analysts using multiple...
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