This dataset corresponds to global map of built-up areas expressed in terms of a probability grid at 10 m spatial resolution derived from a Sentinel-2 global image composite (GHS_composite_S2_L1C_2017-2018_GLOBE_R2020A_UTM_10_v1_0) for reference year 2018. It builds on a new Deep Learning framework for pixel-wise large-scale classification of built-up areas named GHS-S2Net (GHS stands for Global Human Settlements, S2 refers to the Sentinel-2 satellite).
Corbane, Christina; Sabo, Filip; Politis, Panagiotis; Syrris, Vasileios (2026): GHS-BUILT-S2 R2020A - GHS built-up grid, derived from Sentinel-2 global image composite for reference year 2018 using Convolutional Neural Networks (GHS-S2Net) - OBSOLETE RELEASE. European Commission, Joint Research Centre [Dataset] doi: 10.2905/JRC.PKAXH30; 10.2905/016D1A34-B184-42DC-B586-E10B915DD863 PID: http://data.europa.eu/89h/016d1a34-b184-42dc-b586-e10b915dd863
Artificial IntelligenceBig DataBuilt-up areasCopernicusGHS-BUILTGHSLGlobalRemote SensingSentinel-2
This dataset corresponds to global map of built-up areas expressed in terms of a probability grid at 10 m spatial resolution derived from a Sentinel-2 global image composite (GHS_composite_S2_L1C_2017-2018_GLOBE_R2020A_UTM_10_v1_0) for reference year 2018. It builds on a new Deep Learning framework for pixel-wise large-scale classification of built-up areas named GHS-S2Net (GHS stands for Global Human Settlements, S2 refers to the Sentinel-2 satellite).
Projection: Local UTM
Data type: uint 8bit
Built-up probability values rescaled in the range 0-100
Nodata value: 255
GHS_BUILT_S2comp2018_GLOBE_R2020A products technical details
Tiles schema used to produce the GHS_BUILT_S2comp2018_GLOBE_R2020A
Web Mapping Service
Visualization of the layer on JRC Big Data Plaform JEODPP
(under Products/LandCover)
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Spatially consistent and up-to-date maps of human settlements are crucial for addressing policies related to urbanization and sustainability especially in the era of an increasingly urbanized world. The availability of open and free Sentinel-2 data of the Copernicus Earth Observation programme offers a new opportunity for wall-to-wall mapping of human settlements at a global scale. This paper presents a deep-learning-based framework for a fully automated extraction of built-up areas at a spatial resolution of 10 meters from a global composite of Sentinel-2 imagery. A multi-neuro modelling methodology, building on a simple Convolution Neural Networks architecture for pixel-wise image classification of built-up areas is developed. The deployment of the model on the global Sentinel-2 image composite provides the most detailed and complete map reporting about built-up areas for reference year 2018. The validation of the results with an independent reference dataset of building footprints covering 277 sites across the world, establishes the reliability of the built-up layer produced by the proposed framework and the model robustness. The results of this study contribute to cutting-edge research in the field of automated built-up areas mapping from remote sensing data and establish a new reference layer for the analysis of the spatial distribution of human settlements across the rural-urban continuum.
| From date | To date |
|---|---|
| 2017-01-01 | 2018-12-31 |
