Sold by: JRC
Open data
|
Deployed on AWS
The spatial raster dataset delineates the boundaries of the human settlements at 10m resolution, and describe their inner characteristics in terms of the morphology of the built environment and the functional use. The Morphological Settlement Zone (MSZ) delineates the spatial domain of all the human settlements at the neighboring scale of approx. 100m, based on the spatial generalization of the built-up surface fraction (BUFRAC) function. The objective is to fill the open spaces that are surrounded by large patches of built space. MSZ, open spaces, and built spaces basic class abstractions are derived by mathematical morphology spatial filtering (opening, closing, regional maxima) from the BUFRAC function. They are further classified accordingly to the information regarding vegetation intensity, water surfaces (GHS_LAND_GLOBE_R2022A), road surfaces (OSM highways), functional use (GHS-BUILT-C_FUN_GLOBE_R2023A), and building height (GHS-BUILT-H_GLOBE_R2023A). The FUN provides RES vs[...]
Overview
The spatial raster dataset delineates the boundaries of the human settlements at 10m resolution, and describe their inner characteristics in terms of the morphology of the built environment and the functional use. The Morphological Settlement Zone (MSZ) delineates the spatial domain of all the human settlements at the neighboring scale of approx. 100m, based on the spatial generalization of the built-up surface fraction (BUFRAC) function. The objective is to fill the open spaces that are surrounded by large patches of built space. MSZ, open spaces, and built spaces basic class abstractions are derived by mathematical morphology spatial filtering (opening, closing, regional maxima) from the BUFRAC function. They are further classified accordingly to the information regarding vegetation intensity, water surfaces (GHS_LAND_GLOBE_R2022A), road surfaces (OSM highways), functional use (GHS-BUILT-C_FUN_GLOBE_R2023A), and building height (GHS-BUILT-H_GLOBE_R2023A). The FUN provides RES vs. NRES functional classification of the built domain defined as BUFRAC>0.
Features and programs
Open Data Sponsorship Program
This dataset is part of the Open Data Sponsorship Program, an AWS program that covers the cost of storage for publicly available high-value cloud-optimized datasets.
Pricing
This is a publicly available data set. No subscription is required.
Legal
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
Delivery details
AWS Data Exchange (ADX)
AWS Data Exchange is a service that helps AWS easily share and manage data entitlements from other organizations at scale.
Open data resources
Available with or without an AWS account.
- How to use
- To access these resources, reference the Amazon Resource Name (ARN) using the AWS Command Line Interface (CLI). Learn more
- Description
- The GHS Morphological Settlement Zone delineation and inner classification raster layer in World Mollweide (EPSG:54009) at 10m spatial resolution. Spatial extent is Global and temporal extent is year 2018
- Resource type
- S3 bucket
- Amazon Resource Name (ARN)
- arn:aws:s3:::jrc-ghsl/ghs-built-c-msz
- AWS region
- eu-central-1
- AWS CLI access (No AWS account required)
- aws s3 ls --no-sign-request s3://jrc-ghsl/ghs-built-c-msz/
- Description
- The GHS RES vs. NRES functional classification raster layer in World Mollweide (EPSG:54009) at 10m spatial resolution. Spatial extent is Global and temporal extent is year 2018
- Resource type
- S3 bucket
- Amazon Resource Name (ARN)
- arn:aws:s3:::jrc-ghsl/ghs-built-c-fun
- AWS region
- eu-central-1
- AWS CLI access (No AWS account required)
- aws s3 ls --no-sign-request s3://jrc-ghsl/ghs-built-c-fun/
- Description
- Notifications for new data
- Resource type
- SNS topic
- Amazon Resource Name (ARN)
- arn:aws:sns:eu-central-1:291461994469:jrc-ghsl-object_created
- AWS region
- eu-central-1
Resources
Vendor resources
Support
Contact
Managed By
How to cite
GHS-BUILT-C was accessed on DATE from https://registry.opendata.aws/ghs-built-c .
License
The GHSL data are provided under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.
Similar products
The spatial raster dataset depicts the distribution of built-up surfaces, expressed as number of square metres. The data report about the total built-up surface and the built-up surface allocated to dominant non-residential (NRES) uses. Data are spatial-temporal interpolated or extrapolated from 1975 to 2030 in 5 years intervals. For the temporal anchor point of 2018 the data is published at 10m as observed from the S2 image data.
The spatial raster dataset depicts the distribution of built-up volumes, expressed as number of cubic metres. The data reports about the total built-up volume and the built-up volume allocated to dominant non-residential (NRES) uses.
Description: The spatial raster dataset depicts the spatial distribution of the building heights as extracted from the filtering of a composite of global digital elevation models (DEM) and the filtering of satellite imagery using linear regression techniques The used input DEMs are the ALOS World 3D - 30m (AW3D30, 2006-2011) and the Shuttle Radar Topography Mission 30m (SRTM30, 2000). The building heights extracted from these sources are updated using the support of the shadow markers extracted from the Sentinel2 image data composite of the year 2018 (GHS-composite-S2 R2020A). The Average of the Gross Building Height is the result of dividing the built-up volume by the grid surface. The Average of the Net Building Height is the result of dividing the built-up volume by the built-up surface.
Sample dataset for BuiltWith Technology Datasets.