Organisation: European Commission, Joint Research Centre
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Title: GHS built-up grid, derived from Landsat, multitemporal (1975-1990-2000-2014), R2018A


Multi-temporal information layer on the presence of built-up surfaces derived from global Landsat satellite data collected from 1975 to 2014, at the native spatial resolution varying from 80 meters (Landsat MSS sensor), 30 meters (Landsat TM sensor), and 15/30 meters (Landsat ETM sensor). The image data collections were prepared by the Global Land Survey (GLS1975, GLS1990, GLS2000) and by the JRC (Landsat 8 collection for 2014). The data was processed by fully automatic and reproducible methods based on statistical learning (Symbolic Machine Learning). No manual or ad-hoc rule-based editing of the results was applied in the post-processing. The product it is provided with a spatial resolution of 30 meters.

Christina Corbane 0000-0002-2670-1302
Aneta Florczyk 0000-0001-8912-1500
Martino Pesaresi 0000-0003-0620-439X
Panagiotis Politis
Vasileios Syrris 0000-0002-2262-0580
How to cite
Corbane, Christina; Florczyk, Aneta; Pesaresi, Martino; Politis, Panagiotis; Syrris, Vasileios (2018):  GHS built-up grid, derived from Landsat, multitemporal (1975-1990-2000-2014), R2018A. European Commission, Joint Research Centre (JRC) [Dataset] doi:10.2905/jrc-ghsl-10007 PID:
Related resources

Data access


    The data are organised in several datasets. The main product (GHS_BUILT_LDSMT_GLOBE_R2018A_3857_30) is a multitemporal built-up grid (built-up classes: 1975, 1990, 2000, 2014 epoch), which has been produced at high resolution (30m). Multi-temporal built-up area classification map: 0 = no data; 1 = water surface; 2 = land no built-up in any epoch; 3 = built-up from 2000 to 2014 epochs; 4 =...


  • publication A New Method for Earth Observation Data Analytics Based on Symbolic Machine Learning

    Pesaresi M; Syrris V; Julea A. A New Method for Earth Observation Data Analytics Based on Symbolic Machine Learning. REMOTE SENSING 8 (5); 2016. p. 399. JRC99747


Additional information
Issue date 2018-07-18
Landing page
Geographic area World
Temporal coverage

From: 1975-01-01 – To: 2014-12-30

Update frequency irregular
Language English
Data theme(s) Regions and cities; Science and technology
EuroVoc domain(s) 36 SCIENCE; 72 GEOGRAPHY
Digital Object Identifier doi:10.2905/jrc-ghsl-10007

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