DATASET

ESM R2019 - European Settlement Map from Copernicus Very High Resolution data for reference year 2015

Collection: GHSL : Global Human Settlement Layer 

Description

The European Settlement Map data is a spatial raster dataset that is mapping human settlements in Europe based on Copernicus Very High Resolution optical coverage for reference year 2015 (VHR_IMAGE_2015). It follows-up on the previous ESM_2012 derived from 2.5 m resolution SPOT-5/6 images acquired in the context of the pan-European GMES/Copernicus (Core_003) dataset for the reference year 2012.

• ESM_BUILT_VHR2015_EUROPE_R2019: classifies the built-up areas at a spatial resolution of 2 meters (EPSG:3035) • ESM_BUILT_VHR2015CLASS_EUROPE_R2019: classifies the built-up areas into residential and non-residential at a spatial resolution of 10 meters (EPSG:3035)

Contact

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

Contributors

How to cite

Corbane, Christina; Sabo, Filip (2019): ESM R2019 - European Settlement Map from Copernicus Very High Resolution data for reference year 2015. European Commission, Joint Research Centre (JRC) [Dataset] doi: 10.2905/8BD2B792-CC33-4C11-AFD1-B8DD60B44F3B PID: http://data.europa.eu/89h/8bd2b792-cc33-4c11-afd1-b8dd60b44f3b

Keywords

Land classification Pan-European Copernicus remote sensing building typology ESM built-up areas GHSL

Data access

ESM_BUILT_VHR2015_EUROPE_R2019
URL 
  • The ESM_2015 is the latest release of the European Settlement Map produced in the frame of the GHSL project. The ESM product exploits the Copernicus VHR_IMAGE_2015 dataset made of satellite images Pleiades, Deimos-02, WorldView-2, WorldView-3, GeoEye-01 and Spot 6/7 ranging from 2014 to 2016.

GHSL website
URL 
  • Project Web site

Publications

Publication
Application of the Symbolic Machine Learning to Copernicus VHR Imagery: The European Settlement Map
Corban, C., Sabo, F., Syrris, V., Kemper, T., Politis, P., Pesaresi, M., Soille, P. and Osé, K., Application of the Symbolic Machine Learning to Copernicus VHR Imagery: The European Settlement Map, IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, ISSN 1545-598X (online), 17 (7), 2020, p. 1153-1157, JRC116923.
  • IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, PISCATAWAY, USA
Publication page 
  • Abstract

    This letter introduces the new European Settlement Map (ESM) workflow, results and validation.

    Unlike the previous ESM versions, it uses the supervised learning combined with the textural and morphological features for built-up area extraction. Input data is the Copernicus very high resolution collection coming from a variety of sensors. The workflow is fully automated and it does not include any postprocessing. For the first time a new layer that classifies non-residential building is derived by using only remote sensing imagery and training data. The built-up area layer is delivered at 2m pixel resolution while the residential/non residential layer is delivered at 10m spatial resolution. More than 46000 scenes were processed and ~6 million km2 of Europe was mapped by using the Big Data infrastructure.

    Validation showed balanced accuracy of 0.81 and 0.91 for level 1 and 2 layers respectively and 0.70 for the non-residential layer.

Publication
Update and improvement of the European Settlement map
F. Sabo, C. Corbane, P. Politis, M. Pesaresi and T. Kemper, "Update and improvement of the European Settlement map," 2019 Joint Urban Remote Sensing Event (JURSE), Vannes, France, 2019, pp. 1-4. doi: 10.1109/JURSE.2019.8808933
URL 

Geographic areas

European Union

Spatial coverage

Type Value
WKT
POLYGON((-31.39 71.27,44.93 71.27,44.93 27.55,-31.39 27.55,-31.39 71.27))

Temporal coverage

From date To date
2014-01-01 2016-12-31

Additional information

Published by
European Commission, Joint Research Centre
Created date
2019-09-10
Modified date
2019-10-02
Issued date
2019-10-01
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/8bd2b792-cc33-4c11-afd1-b8dd60b44f3b
Popularity