derived data ghsl on-demand urban delineation vector xcube
Urban delineation algorithm classify and delineate urban clusters by typologies. Method was developed by Joint Research Centre (JRC) and described in Atlas of Human Planet 2019 The algorithm uses the global-coverage built-up (GHS-BUILT-S_GLOBE_R2022A) and population (GHS-POP_GLOBE_R2022A) raster datasets as the input data (JRC’s GHSL). The algorithm classifies and delineates urban clusters at two hierarchical levels. At the first hierarchical level, the algorithm identifies the High Density Clusters (Urban Centre) and Moderate Density Clusters. At the second hierarchical level, the algorithm identifies the High Density Clusters (Urban Centre), Dense Urban Clusters, Semi-Dense Urban Clusters and Peri-Urban Clusters.To run the urban delineation, navigate to EDC Browser, select the required input parameters (Area of interest, Reference year, Built-up density, Population density and Total population in cluster for Urban Centres, as well as Built-up density, Population density and Total population in cluster for Moderate Density Clusters) and follow the check-out wizard to complete your order. Produced data will be uploaded directly into your folder /result-data in JupyterLab environment on EDC.
Global coverage but intended for areas with built-up coverage and population.
1975, 1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015 and 2020
On-demand
| Name | Description | Type |
|---|---|---|
| AOI | Area of interest | bounding box |
| Reference year | Reference year | integer from 1975 to 2020, 5 years interval |
| Built-up density for Urban Centres | Built-up density (m2/km2) | int, default=500000 |
| Population density for Urban Centres | Population density (people/km2) | int, default=1500 |
| Total population per Urban Centre | Population (people) | int, default=50000 |
| Built-up density for Moderate Density Clusters | Built-up density (m2/km2) | int, default=50000 |
| Population density for Moderate Density Clusters | Population density (people/km2) | int, default=300 |
| Total population per Moderate Density Clusters | Population (people) | int, default=5000 |
| Name | Description | Code by nomenclature |
|---|---|---|
| HDC_UrbanCentre_EPSG4326_RefYear.geojson | Geojson file containing delineated polygons of High Density Clusters (Urban Centres) in selected AOI and reference year. Polygons are projected to WGS84 coordinate reference system. | undefined |
| MDC_UrbanCluster_EPSG4326_RefYear.geojson | Geojson file containing delineated polygons of Moderate Density Clusters in selected AOI and reference year. Polygons are projected to WGS84 coordinate reference system. | undefined |
| DUC_DenseUrbanCluster_EPSG4326_RefYear.geojson | Geojson file containing delineated polygons of Dense Urban Clusters in selected AOI and reference year. Polygons are projected to WGS84 coordinate reference system. | undefined |
| SDUC_SemiDenseUrbanCluster_EPSG4326_RefYear.geojson | Geojson file containing delineated polygons of Semi-Dense Urban Clusters in selected AOI and reference year. Polygons are projected to WGS84 coordinate reference system. | undefined |
| PUC_PeriUrbanCluster_EPSG4326_RefYear.geojson | Geojson file containing delineated polygons of Peri-Urban Clusters in selected AOI and reference year. Polygons are projected to the WGS84 coordinate reference system. | undefined |
Prices are defined based on the selected area of interest (aoi). The minimal cost for initiating request is 100€. Check here for more info about pricing and restrictions.
Creative Commons Attribution 4.0 International License
See all datasets managed by Gisat s.r.o..
GHSLGlobal Human Settlement Layer (GHSL R2022A) on S3GHS-BUILT-S, GHS-POP