DATASET

GHS-BUILT-C R2022A - GHS Settlement Characteristics, derived from Sentinel2 composite (2018) and other GHS R2022A data - OBSOLETE RELEASE

Collection: GHSL : Global Human Settlement Layer 

Description

OBSOLETE RELEASE - The use of the GHSL Data Package 2022 (GHS P2022) is currently not recommended. CHECK FOR THE MOST UPDATED VERSION OF GHSL DATASETS AT https://ghsl.jrc.ec.europa.eu/datasets.php - 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.

Contact

Email
jrc-ghsl-data (at) ec.europa.eu

Contributors

How to cite

Pesaresi, Martino; Politis, Panagiotis (2022): GHS-BUILT-C R2022A - GHS Settlement Characteristics, derived from Sentinel2 composite (2018) and other GHS R2022A data - OBSOLETE RELEASE. European Commission, Joint Research Centre (JRC) [Dataset] doi: 10.2905/DDE11594-2A66-4C1B-9A19-821382AED36E PID: http://data.europa.eu/89h/dde11594-2a66-4c1b-9a19-821382aed36e

Keywords

GHS BUILT CHARACTERISTICS GHS-BUILT-C GHSL global map MSZ Settlement Morphological Zone Settlement spatial delineation Urban green

Data access

GHS-BUILT-C_MSZ_GLOBE_R2022A
URL 
  • GHS Morphological Settlement Zone (MSZ) delineation and inner classification of the morphology of the built environment. The data is published at 10m resolution in World Mollweide (EPSG:54009). The compressed ZIP file contain TIF files and short documentation.

GHS-BUILT-C_FUN_GLOBE_R2022A
URL 
  • Residential (RES) vs. non-residential (NRES) functional classification of the built spatial domain defined as BUFRAC > 0. The data is published at 10m resolution in World Mollweide (EPSG:54009). The compressed ZIP file contain TIF files and short documentation.

GHS-BUILT-C_VEG_GLOBE_R2022A
URL 
  • Vegetation intensity in the Morphological Settlement Zone (MSZ). The data is published at 10m resolution in World Mollweide (EPSG:54009). The compressed ZIP file contain TIF files and short documentation.

GHSL website
URL 
  • Project Web site

Publications

Publication 2022
GHSL Data Package 2022
Schiavina, M., Melchiorri, M., Pesaresi, M., Politis, P., Carneiro Freire, S.M., Maffenini, L., Florio, P., Ehrlich, D., Goch, K., Tommasi, P. and Kemper, T., GHSL Data Package 2022, Publications Office of the European Union, Luxembourg, 2022, ISBN 978-92-76-53071-8 (online),978-92-76-53070-1 (print), doi:10.2760/19817 (online),10.2760/526478 (print), JRC129516.
  • Publications Office of the European Union, Luxembourg, Luxembourg
Publication page 
  • Abstract

    The Global Human Settlement Layer (GHSL) produces new global spatial information, evidence-based analytics and knowledge describing the human presence on planet Earth. It operates in a fully open and free data and methods access policy. The knowledge generated with the GHSL is supporting the definition, the public discussion and the implementation of European policies and the monitoring of international frameworks such as the 2030 Development Agenda. The GHSL are the core data set of the Exposure Mapping Component under the Copernicus Emergency Management Service. This release is the first official contribution of GHSL to the Copernicus services. GHSL data continue to support the GEO Human Planet Initiative (HPI) that is committed to developing a new generation of measurements and information products providing new scientific evidence and a comprehensive understanding of the human presence on the planet and that can support global policy processes with agreed, actionable and goal-driven metrics. The Human Planet Initiative relies on a core set of partners committed in coordinating the production of the global settlement spatial baseline data. This document describes the public release of the GHSL Data Package 2022 (GHS P2022). The release provides improved built-up (including surface, volume and height) and population products as well as a new settlement model and classification of administrative and territorial units according to the Degree of Urbanisation.

Publication 2021
Generalized vertical components of built-up areas from global Digital Elevation Models by multi-scale linear regression modelling
Pesaresi, M., Corban, C., Ren, C. and Edward, N., Generalized vertical components of built-up areas from global Digital Elevation Models by multi-scale linear regression modelling, PLOS ONE, ISSN 1932-6203 (online), 16 (2), 2021, p. e0244478, JRC117825.
  • PUBLIC LIBRARY SCIENCE, SAN FRANCISCO, USA
Publication page 
  • Abstract

    The estimation of the vertical components of built-up areas from free Digital Elevation Model (DEM) global data filtered by multi-scale convolutional, morphological and textural transforms are generalized at the resolution of 250 meters using linear least-squares regression techniques. Six test cases were selected: Hong Kong, London, New York, San Francisco, Sao Paulo, and Toronto. Five global DEM and two DEM composites are evaluated in terms of 60 combinations of linear, morphological and textural filtering and different generalization techniques. Four generalized vertical components estimates of built-up areas are introduced: the Average Gross Building Height (AGBH), the Average Net Building Height (ANBH), the Standard Deviation of Gross Building Height (SGBH), and the Standard Deviation of Net Building Height (SNBH). The study shows that the best estimation of the net GVC of built-up areas given by the ANBH and SNBH, always contains a greater error than their corresponding gross GVC estimation given by the AGBH and

    SGBH, both in terms of mean and standard deviation. Among the sources evaluated in this study, the best DEM source for estimating the GVC of built-up areas with univariate linear regression techniques is a composite of the 1-arcsec Shuttle Radar Topography Mission (SRTM30)

    and the Advanced Land Observing Satellite (ALOS) World 3D–30 m (AW3D30) using the union operator (CMP_SRTM30-AW3D30_U). A multivariate linear model was developed using 16 satellite features extracted from the CMP_SRTM30-AW3D30_U enriched by other land cover sources, to estimate the gross GVC. A RMSE of 2.40 m and 3.25 m was obtained for the AGBH and the SGBH, respectively. A similar multivariate linear model was developed to estimate the net GVC. A RMSE of 6.63 m and 4.38 m was obtained for the ANBH and the SNBH, respectively. The main limiting factors on the use of the available global DEMs for estimation the GVC of built-up areas are two. First, the fact that the horizontal resolution of these sources (circa 30 and 90 meters) corresponds to a sampling size that is larger than the expected average horizontal size of built-up structures as detected from nadir-angle EO data, producing more reliable estimates for gross vertical components than for net vertical component of built-up areas. Second, post-production processing targeting Digital Terrain Model specifications may purposely filter out the information on the vertical component of built-up areas that are contained in the global DEMs.

    Under the limitations of the study presented here, these results show a potential for using global DEM sources in order to derive statistically generalized parameters describing the vertical characteristics of built-up areas, at the scale of 250x250 meters. However, estimates need to be evaluated in terms of the specific requirements of target applications such as spatial population modelling, urban morphology, climate studies and so on.

Publication 2020
The Generalised Settlement Area: mapping the Earth surface in the vicinity of built-up areas
Florczyk, A., Melchiori, M., Zeidler, J., Corban, C., Schiavina, M., Carneiro Freire, S., Sabo, F., Politis, P., Esch, T. and Pesaresi, M., The Generalised Settlement Area: mapping the Earth surface in the vicinity of built-up areas, INTERNATIONAL JOURNAL OF DIGITAL EARTH, ISSN 1753-8947 (online), 13 (1), 2020, p. 45-60, JRC113584.
  • TAYLOR & FRANCIS LTD, CHINA MAINLAND
Publication page 
  • Abstract

    Geo-information on settlements from Earth Observation offers a base for objective and scalable monitoring of evolution of cities and settlements, including their location, extent and other attributes. In this work, we deploy the best available global knowledge on the presence of human settlements and built-up structures derived from Earth Observation to advance the understanding of the human presence on Earth. We start from a concept of Generalised Settlement Area to identify the Earth surface within which any built-up structure is present. We further characterise the resulted map by using an agreement map among the state of the art of remote sensing products mapping built-up areas or other strictly related semantic abstractions as urban areas or artificial surfaces. The agreement map is formed by a grid of 1 km2, where each cell is classified according to the number of EO-derived products reporting any positive occurrence of the abstractions related to the presence of built-up structures. The paper describes the characteristics of the Generalised Settlement Area, the differences in the agreement map across geographic regions of the world, and outlines the implications for potential users of the EO-derived products used in this study.

Publication 2016
Operating procedure for the production of the Global Human Settlement Layer from Landsat data of the epochs 1975, 1990, 2000, and 2014
Pesaresi M, Ehrlich D, Ferri S, Florczyk A, Carneiro Freire S, Halkia S, Julea A, Kemper T, Soille P, Syrris V. Operating procedure for the production of the Global Human Settlement Layer from Landsat data of the epochs 1975, 1990, 2000, and 2014. EUR 27741. Luxembourg (Luxembourg): Publications Office of the European Union; 2016. JRC97705
  • Publications Office of the European Union, Luxembourg, Luxembourg
Publication page 
  • Abstract

    A new global information baseline describing the spatial evolution of the human settlements in the past 40 years is presented. It is the most spatially global detailed data available today dedicated to human settlements, and it shows the greatest temporal depth. The core processing methodology relies on a new supervised classification paradigm based on symbolic machine learning.

    The information is extracted from Landsat image records organized in four collections corresponding to the epochs 1975, 1990, 2000, and 2014. The experiment reported here is the first known attempt to exploit global Multispectral Scanner data for historical land cover assessment. As primary goal, the Landsat-made Global Human Settlement Layer (GHSL) reports about the presence of built-up areas in the different epochs at the spatial resolution allowed by the Landsat sensor. Preliminary tests confirm that the quality of the information on built-up areas delivered by GHSL is better than other available global information layers extracted by automatic processing from Earth Observation data. An experimental multiple-class land-cover product is also produced from the epoch 2014 collection using low-resolution space-derived products as training set. The classification schema of the settlement distinguishes built-up areas based on vegetation contents and volume of buildings, the latter estimated from integration of SRTM and ASTER-GDEM data. On the overall, the experiment demonstrated a step forward in production of land cover information from global fine-scale satellite data using automatic and reproducible methodology.

Publication 2016
Assessment of the Added-Value of Sentinel-2 for Detecting Built-up Areas
Pesaresi M, Corban C, Julea A, Florczyk A, Syrris V, Soille P. Assessment of the Added-Value of Sentinel-2 for Detecting Built-up Areas. REMOTE SENSING 8 (4); 2016. p. 299. JRC99996
  • MDPI AG, BASEL, SWITZERLAND
Publication page 
  • Abstract

    Monitoring of the human-induced changes and the availability of reliable and methodologically consistent urban area maps are essential to support sustainable urban development on a global scale. The Global Human Settlement Layer (GHSL) is a project funded by the European Commission, Joint Research Centre, which aims at providing scientific methods and systems for reliable and automatic mapping of built-up areas from remote sensing data. In the frame of the GHSL, the opportunities offered by the recent availability of Sentinel-2 data are being explored using a novel image classification method, called Symbolic Machine Learning (SML), for detailed urban land cover mapping. In this paper, a preliminary test was implemented with the purpose of: (i) assessing the applicability of the SML classifier on Sentinel-2 imagery; (ii) evaluating the complementarity of Sentinel-1 and Sentinel-2; and (iii) understanding the added-value of Sentinel-2 with respect to Landsat for improving global high-resolution human settlement mapping. The overall objective is to explore areas of improvement, including the possibility of synergistic use of the different sensors. The results showed that noticeable improvement of the quality of the classification could be gained from the increased spatial detail and from the thematic contents of Sentinel-2 compared to the Landsat derived product as well as from the complementarity between Sentinel-1 and Sentinel-2 images.

Geographic areas

World

Spatial coverage

Type Value
GML
<gml:Polygon xmlns:gml="http://www.opengis.net/gml">  <gml:outerBoundaryIs>    <gml:LinearRing>      <gml:coordinates>180,90 -180,90 -180,-90 180,-90 180,90</gml:coordinates>    </gml:LinearRing>  </gml:outerBoundaryIs></gml:Polygon>
GML
<gml:Polygon xmlns:gml="http://www.opengis.net/gml/3.2">  <gml:exterior>    <gml:LinearRing>      <gml:posList>180 90 -180 90 -180 -90 180 -90 180 90</gml:posList>    </gml:LinearRing>  </gml:exterior></gml:Polygon>
WKT
POLYGON ((180 90, -180 90, -180 -90, 180 -90, 180 90))

Temporal coverage

From date To date
2018-01-01 2018-12-31

Additional information

Published by
European Commission, Joint Research Centre
Created date
2022-06-01
Modified date
2023-05-08
Issued date
2022-06-25
Landing page
http://ghsl.jrc.ec.europa.eu/ 
Language(s)
English
Data theme(s)
Regions and cities, Science and technology
Update frequency
irregular
Identifier
http://data.europa.eu/89h/dde11594-2a66-4c1b-9a19-821382aed36e
Popularity