Information layer on the presence of built-up surfaces derived from global Sentinel-1 Synthetic Aperture Radar (SAR) satellite data, collected during 2016. The native spatial resolution of the data is 20 meters with a pixel spacing of 10 meters and dual polarisation acquisitions (VV and VH). 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 20 meters.
Corbane, Christina; Politis, Panagiotis; Syrris, Vasileios; Pesaresi, Martino (2026): GHS built-up grid, derived from Sentinel-1 (2016), R2018A - OBSOLETE RELEASE. European Commission, Joint Research Centre [Dataset] doi: 10.2905/JRC.4XTWAZ8; 10.2905/jrc-ghsl-10008 PID: http://data.europa.eu/89h/jrc-ghsl-10008
big databuilt-upCopernicusearth observationGHSLglobal mapSentinel-1urban
The grid is provided as a VRT file (with GeoTIFF tiles), and with pyramids. Classification map depicting built-up presence: 0 = no built-up or no data; 1 = built-up area. Spatial resolution: 20m; CRS: EPSG:3857 (Pseudo Mercator).
Continuous global-scale mapping of human settlements in the service of international agreements calls for massive volume of multi-source, multi-temporal and multi-scale earth observation data. In this paper, the latest developments in terms of processing big earth observation data for the purpose of improving the Global Human Settlement Layer (GHSL) data are presented. Two experiments with Sentinel-1 and Landsat data collections were run leveraging on the Joint Research Centre (JRC) Earth Observation Data and Processing Platform (JEODPP). A comparative analysis of the results of built-up areas extraction from different remote sensing data and processing workflows shows how the information production supported by data-intensive computing infrastructure for optimization and multiple testing can improve the output information reliability and consistency within the GHSL scope. The paper presents the processing workflows and the results of the two main experiments giving insights into the enhanced mapping capabilities gained by analyzing Sentinel-1 and Landsat datasets, and the lessons learnt in terms of handling and processing big earth observation data
This work introduces a new classification method in the remote sensing domain, suitably
adapted to dealing with the challenges posed by the big data processing and analytics framework.
The method is based on symbolic learning techniques, and it is designed to work in complex and
information-abundant environments, where relationships among different data layers are assessed
in model-free and computationally-effective modalities. The two main stages of the method are the
data reduction-sequencing and the association analysis. The former refers to data representation; the
latter searches for systematic relationships between data instances derived from images and spatial
information encoded in supervisory signals. Subsequently, a new measure named the evidence-based
normalized differential index, inspired by the probability-based family of objective interestingness
measures, evaluates these associations. Additional information about the computational complexity
of the classification algorithm and some critical remarks are briefly introduced. An application of
land cover mapping where the input image features are morphological and radiometric descriptors
demonstrates the capacity of the method; in this instructive application, a subset of eight classes from
the Corine Land Cover is used as the reference source to guide the training phase.
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