1책임연구원, 한국지질자원연구원 지질자원데이터센터, 대전광역시 유성구 과학로 124, 34132, 대한민국
2박사과정생, 한국지질자원연구원 지질자원데이터센터, 대전광역시 유성구 과학로 124, 34132, 대한민국
1Principal Researcher, Geoscience Data Center, Korea Institute of Geoscience and Mineral Resources, 124 Gwahak-ro, Yuseong-gu, 34132 Daejeon, South Korea
2Ph.D. Student, Geoscience Data Center, Korea Institute of Geoscience and Mineral Resources, 124 Gwahak-ro, Yuseong-gu, 34132 Daejeon, South Korea
Copyright © 2023 GeoAI Data Society
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Conflict of Interest
Saro Lee has been an Editorial Board of GEO DATA; however, he was not involved in the peer reviewer selection, evaluation, or decision process of this paper. Otherwise, no other potential conflicts of interest relevant to this paper were reported.
Funding Information
This research was supported by the Basic Research Project of the Korea Institute of Geoscience and Mineral Resources (KIGAM) and the National Research Foundation of Korea (NRF) grant funded by Korea government (MSIT) (No. 2023R1A2C1003095).
Data Availability Statement
The data that support the findings of this study are openly available in site of following. Geo-environmental factor: TWI (USGS DEM); Slope, Plan Curvature, TPI (https://www.earthenv.org/topography); Rainfall (https://www.worldclim.org); Distance to River (https://www.hydrosheds.org/); Land Use (https://www.fao.org); Temperature (https://cds.climate.copernicus.eu/); Lithology (https://ccgm.org/en/); Soil Texture, Soil Moisture (https://openlandmap.org/).
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.
Geo-environmental factor | Source | Spatial resolution |
---|---|---|
TWI | USGS DEM | 1×1 km |
Slope, plan curvature, TPI | https://www.earthenv.org/topography | 1×1 km |
Rainfall | https://www.worldclim.org | 1×1 km |
Distance to river | https://www.hydrosheds.org/ | 250×250 m |
Land use | https://www.fao.org | 1×1 km |
Temperature | https://cds.climate.copernicus.eu/ | 1×1 km |
Lithology | https://ccgm.org/en/ | 5×5 km |
Soil texture, soil moisture | https://openlandmap.org/ | 1×1 km |
Sort | Field | Subcategory#1 | Subcategory#2 |
---|---|---|---|
Essential | *Title | Global Flood and Landslide Location Data, Influencing Environmental Data | |
*DOI name | USGS DEM, https://www.earthenv.org/topography, https://www.worldclim.org, https://www.hydrosheds.org/, https://www.fao.org, https://cds.climate.copernicus.eu/, https://ccgm.org/en/, https://openlandmap.org/ | ||
*Category | Geoscientific Information | ||
Abstract | Geospatial Data for Flood and Landslide Susceptibility Mapping | ||
*Temporal Coverage | 1970-2021 | ||
*Spatial Coverage | Global | ||
WGS84 Coordinates | Point/Grid | ||
*Personnel | Name | Saro Lee | |
Affiliation | Korea Institute of Geoscience and Mineral Resources | ||
leesaro@kigam.re.kr | |||
*CC License | CC BY-NC-ND | ||
Optional | *Project | Development of Optimized Machine Learning Application Techniques for Spatial Prediction in Natural Hazard (Flood and Landslide) | |
*Instrument |
Geo-environmental factor | Source | Spatial resolution |
---|---|---|
TWI | USGS DEM | 1×1 km |
Slope, plan curvature, TPI | 1×1 km | |
Rainfall | 1×1 km | |
Distance to river | 250×250 m | |
Land use | 1×1 km | |
Temperature | 1×1 km | |
Lithology | 5×5 km | |
Soil texture, soil moisture | 1×1 km |
Sort | Field | Subcategory#1 | Subcategory#2 |
---|---|---|---|
Essential | *Title | Global Flood and Landslide Location Data, Influencing Environmental Data | |
*DOI name | USGS DEM, |
||
*Category | Geoscientific Information | ||
Abstract | Geospatial Data for Flood and Landslide Susceptibility Mapping | ||
*Temporal Coverage | 1970-2021 | ||
*Spatial Coverage | Global | ||
WGS84 Coordinates | Point/Grid | ||
*Personnel | Name | Saro Lee | |
Affiliation | Korea Institute of Geoscience and Mineral Resources | ||
leesaro@kigam.re.kr | |||
*CC License | CC BY-NC-ND | ||
Optional | *Project | Development of Optimized Machine Learning Application Techniques for Spatial Prediction in Natural Hazard (Flood and Landslide) | |
*Instrument |
TWI, topographic wetness index; DEM, digital elevation model; TPI, topographic position index.