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GHSL - Global Human Settlement Layer

Open and free data and tools for assessing the human presence on the planet

New release: GHS P2022

This page is here only for historical reason, please read the disclaimer below.

Introduction

UPDATE (8/05/2023): The new data is now available as part of the GHSL release R2023A. Please browse the updated version of the GHSL datasets list to get the new data.

The Global Human Settlement Layer (GHSL) project produces global spatial information, evidence-based analytics, and knowledge describing the human presence on the planet.

The GHSL relies on the design and implementation of new spatial data mining technologies that allow automatic processing, data analytics and knowledge extraction from large amounts of heterogeneous data including global, fine-scale satellite image data streams, census data, and crowd sourced or volunteered geographic information sources.

The section below contains a list of the new datasets released with the links to the datasheets.

A summary of the information about the new GHSL data released is also available, with links to the datasheets and how to cite and download information.

A series of images depicting the data extraction process of the new data produced
GHSL process (GHS P2022) from satellite imagery to classification of administrative units according to the Degree of Urbanisation in the area of Katowice (Poland). From the left to the right: Sentinel-2 2018 imagery; GHS-BUILT-S 100m built-up detection; GHS-POP 100m population distribution; GHS-SMOD 1000m settlement classification grid; GHS-DUC administrative units classification.

The data

The GHSL Data Package 2022 consists of multitemporal products, that offers an insight into the human presence in the past (epochs from 1975 through 2020, 5 years interval) and the future (2025 and 2030):

  • GHS-BUILT-S R2022A - GHS built-up surface grid, derived from Sentinel-2 composite (2018) and Landsat, multitemporal (1975-2030, 5 years interval)
  • GHS-BUILT-H R2022A - GHS building height, derived from AW3D30, SRTM30, and Sentinel-2 composite (2018)
  • GHS-BUILT-V R2022A - GHS built-up volume grids derived from joint assessment of Sentinel-2, Landsat, and global DEM data, multitemporal (1975-2030, 5 years interval)
  • GHS-BUILT-C R2022A - GHS Settlement Characteristics, derived from Sentinel-2 composite (2018) and other GHS R2022A data
  • GHS-LAND R2022A - Land fraction as derived from Sentinel-2 image composite (2018) and OSM data
  • GHS-POP R2022A - GHS population grid multitemporal (1975-2030, 5 years interval)
  • GHS-SMOD R2022A - GHS settlement layers, application of the Degree of Urbanisation methodology (stage I) to GHS-POP R2022A and GHS-BUILT-S R2022A, multitemporal (1975-2030, 5 years interval)
  • GHS-DUC R2022A - GHS Degree of Urbanisation Classification, application of the Degree of Urbanisation methodology (stage II) to GADM 3.6 layer, multitemporal (1975-2030, 5 years interval)
  • GHS-SDATA R2022A - GHS release R2022A supporting data
  • GHS-BUILT-LAU2STAT R2022A - GHS built-up surface statistics in European LAU2, multitemporal (1975-2020, 5 years interval)

Feel free to Download the GHSL Data Package 2022 report (9.87 MB) containing the complete information about GHSL open data.

Information concerning multi-temporal analyses

The use of products included the GHSL Data Package 2022 (GHS P2022) is currently not recommended.

Although tests with independent reference data show that GHSL R2022A matches or outperforms other data sources for accuracy in epochs 2018 and 2020 and matches or outperforms also all the other single epochs (1975, 1990, 2000, and 2015) included in the previous release GHSL R2019, the accuracy of the time series and its change rate are lower, especially in the rural domain. According to the JRC internal tests, the anomaly is expected to introduce a positive bias in predicted change rates of built-up surfaces and built-up volumes after the year 2000. The positive bias is especially remarkable in the rural domain as set by the GHS-SMOD R2022A. Previous GHSL R2019 was affected by larger omission errors in rural areas compared to R2022A, therefore generating an underestimation of the change rates in the rural domain

Thus, the use of the GHSL Data Package 2022 (GHS P2022) is currently not recommended for supporting multi-temporal studies and indicators including built-up surfaces, built-up volumes, and population, especially if stratified by GHS-SMOD grid class. Applications relying on data for epochs 2018 and 2020 are not affected.

The products, based on the new multi-temporal model that has been developed, have been processed at global level and are available as part of the GHSL release R2023A. Please browse the updated version of the GHSL datasets list to get the new data.

We thank all GHSL users for your continued support and apologize for the inconvenience.