Geospatial data

GHSL Degree of Urbanisation settlement model (GHS-SMOD)

These gridded datasets represent the “Degree of Urbanisation” (DEGURBA) settlement model per 1-km grid cell from 1975 to 2030 in 5-year time steps, derived from the GHS-POP R2023A gridded population estimates and GHS-BUILT-S gridded estimates of built-up surface. The DEGURBA methodology has been endorsed by the UN Statistical Commission and categorizes the Earth's surface into either three broad classes (level 1) or seven more detailed classes (level 2), capturing the spectrum from rural to urban areas. For instance, level one differentiates between urban centres, peri-urban or semi-dense areas, and rural areas. The Degree of...

Listed for discovery. Access is provided by the original publisher under its terms.

Data coverage

These gridded datasets represent the “Degree of Urbanisation” (DEGURBA) settlement model per 1-km grid cell from 1975 to 2030 in 5-year time steps, derived from the GHS-POP R2023A gridded population estimates and GHS-BUILT-S gridded estimates of built-up surface. The DEGURBA methodology has been endorsed by the UN Statistical Commission and categorizes the Earth's surface into either three broad classes (level 1) or seven more detailed classes (level 2), capturing the spectrum from rural to urban areas. For instance, level one differentiates between urban centres, peri-urban or semi-dense areas, and rural areas. The Degree of Urbanisation method is based on segmentation of gridded population data (GHS-POP R2023A) and gridded built-up surface estimates from GHS-BUILT-S R2023A). These datasets represent the Degree of Urbanisation settlement model at the detailed level 2. The level-1 classification can be obtained by class aggregation. Class codes: 10 (water), 11 (Very low density rural area), 12 (Low density rural area), 13 (Rural cluster), 21 (Suburban or periurban area), 22 (Semi-dense urban cluster), 23 (Dense urban cluster), 30 (Urban centre).

Access & permitted use

Review the publisher’s documentation for availability, eligibility and permitted uses, including commercial analysis, model training and redistribution.

GHSL data are provided under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

Need help sourcing data?

Tell us your required fields, population, geography and time period. We can review suitable sources and licensing options.

Request sourcing help

Related data sources

Climate and weatherPublic download

NOAA Global Daily Climate Observations

Daily precipitation, temperature, snowfall and other surface observations from the Global Historical Climatology Network across international stations.

NOAA National Centers for Environmental Information
Climate & weatherExternal source; review access terms

(EXPERIMENTAL) NOAA FourCastNet Global Forecast System (FourCastNetGFS) (EXPERIMENTAL)

The FourCastNet Global Forecast System (FourCastNetGFS) is an experimental system set up by the National Centers for Environmental Prediction (NCEP) to produce medium range global forecasts. The model runs on a 0.25 degree latitude-longitude grid (about 28 km) and 13 pressure levels. The model produces forecasts 4 times a day at 00Z, 06Z, 12Z and 18Z cycles. Major atmospheric and surface fields including temperature, wind components, geopotential height, relative humidity and 2 meter temperature and 10 meter winds are available. The products are 6 hourly forecasts up to 10 days. The data format is GRIB2. The FourCastNetGFS...

NOAA
Geospatial dataExternal source; review access terms

10m Annual Land Use Land Cover (9-class)

This dataset, produced by Impact Observatory, Microsoft, and Esri, displays a global map of land use and land cover (LULC) derived from ESA Sentinel-2 imagery at 10 meter resolution for the years 2017 - 2024. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year. This dataset was generated by Impact Observatory, which used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification. Each global map was produced by applying this model to the Sentinel-2 annual scene...

Impact Observatory