GHS-BUILT-H
cities classification cog disaster response earth observation geoscience geospatial global mapping planetary tiff tiles
Description
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.
Update Frequency
NA
License
The GHSL data are provided under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.
Documentation
https://human-settlement.emergency.copernicus.eu/ghs_buH2023.php
Managed By
See all datasets managed by JRC.
Contact
How to Cite
GHS-BUILT-H was accessed on DATE from https://registry.opendata.aws/ghs-built-h.
Usage Examples
Tutorials
Publications
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Advances on the Global Human Settlement Layer by joint assessment of Earth Observation and Population Survey data by PESARESI Martino, SCHIAVINA Marcello, POLITIS Panagiotis, FREIRE Sergio, GOCH Katarzyna, UHL Johannes H., CARIOLI Alessandra, CORBANE Christina, DIJKSTRA Lewis, FLORIO Pietro, FRIEDRICH Hannah K., GAO Jing, LEYK Stefan, LINLIN Lu, MAFFENINI Luca, MARI-RIVERO Ines, MELCHIORRI Michele, SYRRIS Vasileios, VAN DEN HOEK Jamon, KEMPER Thomas
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Generalized vertical components of built-up areas from global Digital Elevation Models by multi-scale linear regression modelling by PESARESI Martino, CORBAN Christina, REN Chao, EDWARD Ng
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GHSL Data Package 2023 by SCHIAVINA Marcello, MELCHIORRI Michele, PESARESI Martino, POLITIS Panagiotis, CARNEIRO FREIRE Sergio Manuel, MAFFENINI Luca, FLORIO Pietro, EHRLICH Daniele, GOCH Katarzyna, CARIOLI Alessandra, UHL Johannes, TOMMASI Pierpaolo, KEMPER Thomas
Resources on AWS
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- Description
- The GHS Average of the Gross Building raster layer in World Mollweide (EPSG:54009) at 100m spatial resolution; also available in WGS 1984 (EPSG:4326) at 3ss 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-h-agbh- AWS Region
eu-central-1- AWS CLI Access (No AWS account required)
aws s3 ls --no-sign-request s3://jrc-ghsl/ghs-built-h-agbh/- Explore
- Browse Bucket
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- Description
- The GHS Average of the Net Building raster layer in World Mollweide (EPSG:54009) at 100m spatial resolution; also available in WGS 1984 (EPSG:4326) at 3ss 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-h-anbh- AWS Region
eu-central-1- AWS CLI Access (No AWS account required)
aws s3 ls --no-sign-request s3://jrc-ghsl/ghs-built-h-anbh/- Explore
- Browse Bucket
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- 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