The spatial raster dataset depicts the distribution of the built-up (BU) surfaces estimates between 1975 and 2030 in 5 years intervals and two functional use components a) the total BU surface and b) the non-residential (NRES) BU surface. The data is made by spatial-temporal interpolation of five observed collections of multiple-sensor, multiple-platform satellite imageries. Landsat (MSS, TM, ETM sensor) supports the 1975, 1990, 2000, and 2014 epochs. Sentinel2 (S2) composite (GHS-composite-S2 R2020A) supports the 2018 epoch.
The built-up surface fraction (BUFRAC) is estimated at 10m of spatial resolution from the S2 image data, using as learning set a composite of data from GHS-BUILT-S2 R2020A, Facebook, Microsoft, and Open Street Map (OSM) building delineation. The BUFRAC inference is made from the combination of quantized image features (reflectance, derivative of morphological profile DMP) through associative rule learning applied to spatial data analytics, which was introduced as symbolic machine learning (SML). The non-residential (NRES) domain is predicted from S2 image data by observation of radiometric, textural, and morphological features in an object-oriented image processing framework. The multi-temporal dimension is provided by testing by the SML the association between the combination of the quantized radiometric information collected by the Landsat imagery in the past epochs, and the “built-up” (BU) and “non-built-up” (NBU) class abstraction on image segments extracted from S2 images. The spatial-temporal interpolation is solved by rank-optimal spatial allocation using explanatory variables related to the landscape (slope, elevation, distance to water, and distance to vegetation) and related to the observed dynamic of BU surfaces in the past epochs.
- Panagiotis Politis
How to cite
Pesaresi, Martino; Politis, Panagiotis (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) [Dataset] doi: 10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EA PID: http://data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea
GHS non-residential (NRES) built-up surface grids derived from joint assessment of Sentinel2 and Landsat sensors, for 1975-2030 (5yrs interval). Values are expressed in unsigned integers and report about the predicted amount of built-up square meters. The data is published at 100m and 1km resolution in World Mollweide (EPSG:54009). For the year 2018 the data is published at 10m as observed from the S2 image data
GHS built-up surface grids derived from joint assessment of Sentinel2 and Landsat sensors, for 1975-2030 (5yrs interval). Values are expressed in unsigned integers and report about the predicted amount of built-up square meters. The data is published at 100m and 1km resolution in World Mollweide (EPSG:54009). For the year 2018 the data is published at 10m as observed from the S2 image data.
- Publications Office of the European Union, Luxembourg, Luxembourg
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. 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 2023 (GHS P2023). 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.
- MDPI AG, BASEL, SWITZERLAND
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.
- MDPI AG, BASEL, SWITZERLAND
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.
- Publications Office of the European Union, Luxembourg, Luxembourg
Global fine-scale information extraction tasks using today’s remote sensing technologies are compared with Big Data Analytics tasks. Issues related to data classification, machine learning and evaluation of the results are discussed. A change of paradigm respect to the classical RS information processing model is proposed in order to cope with the Big RS Data characteristics.
- IEEE COMPUTER SOC, LOS ALAMITOS, USA
Differential area profiles (DAPs) are point-based multiscale descriptors used in pattern analysis and image segmentation. They are defined through sets of size-based connected morphological filters that constitute a joint area opening top-hat and area closing bottom-hat scale-space of the input image. The work presented in this paper explores the properties of this image decomposition through sets of area zones. An area zone defines a single plane of the DAP vector field and contains all the peak components of the input image, whose size is between the zone’s attribute extrema. Area zones can be computed efficiently from hierarchical image representation structures, in a way similar to regular attribute filters. Operations on the DAP vector field can then be computed without the need for exporting it first, and an example with the leveling-like convex/concave segmentation scheme is given. This is referred to as the one-pass method and it is demonstrated on the Max-Tree structure. Its computational performance is tested and compared against conventional means for computing differential profiles, relying on iterative application of area openings and closings. Applications making use of the area zone decomposition are demonstrated in problems related to remote sensing and medical image analysis.
- IEEE , Piscataway, USA
The location and identification of human settlements is a key information in any assessment related to human security and safety decision process, from the preparedness to and mitigation of natural hazards to postdisaster response and reconstruction. The human settlements can be defined as infrastructures build on earth surface for living, trading or any social activity.
In order to cover and analyze large areas where scarce and heterogeneous data may exist, the automatic information extraction from Earth Observation is the only sustainable strategy. Several studies about the detection of built-up presence from high resolution satellite were performed during the past few years. Among the various methods, a built-up index aggregating anisotropic textural co-occurence measures (PanTex) was demonstrated to be a robust indicator, where 54 high resolution optical images representing cities distributed around the world are processed. The validation of the built-up presence index emphasized its robustness against the type of constructions, the sun angles and the seasons of acquisitions. In this paper, we propose an alternative procedure for the calculation of built-up presence index from panchromatic high resolution satellite images. While radiometric descriptors of human settlements are highly variable across the world and with illumination conditions, the shape of settlements often contains right angles. This property being more stable, we propose a built-up index based on the density of corners. The index is obtained in two steps: - corners are detected by multi scale Harris detector based on differential morphological decompositions; - corners are spatially aggregated to form a density of corners, which is the built-up index. The differential morphological decomposition is a scale-space representation of the image, where image elements are separated by their scales. Then, the Harris corner indicator, which is highly dependent on a scale parameter, can be adapted to a set of scales. The output of the corner detection is a set of points associated to a scale, which represents the right angles in the image. The corners associated to a scale in the range of admissible settlements dimensions are selected and spatially aggregated to derive a density of corners.
The proposed index is extracted from various high resolution panchromatic images and it is compared to the PanTex. In the experimental section, the high correlation between both indicators proves the suitability of the proposed method for consistently detecting built-up presence. Moreover, it gives a new interpretation of the PanTex which is close to a density of corners. Such an observation is critical for understanding the variablity of the PanTex index in between dense urban and industrial areas (industrial areas being composed of large buildings mechanically contain less corners).
- SPIE, U.S.A
A new compact representation of differential morphological profile (DMP) vector fields is presented. It is referred to as the CSL model and is conceived to radically reduce the dimensionality of the DMP descriptors. The model maps three characteristic parameters, namely scale, saliency and level, into the RGB space through a HSV transform. The result is a a medium abstraction semantic layer used for visual exploration, image information mining and pattern classification. Fused with the PANTEX built-up presence index, the CSL model converges to an approximate building footprint representation layer in which color represents building class labels. This process is demonstrated on the first high resolution (HR) global human settlement layer (GHSL) computed from multi-modal HR and VHR satellite images. Results of the first massive processing exercise involving several thousands of scenes around the globe are reported along with validation figures.
- IEEE, Piscataway, United States of America
A procedure for the calculation of a texture-derived built-up presence index (PanTex) from textural characteristics of panchromatic satellite data is presented. The index is based on fuzzy rule-based composition of anisotropic textural co-occurrence measures derived from the satellite data by the gray-level co-occurrence matrix (GLCM). The strength and weakness of the procedure is analyzed and compared with traditional radiometric and textural approaches with the help of specific examples.
A variant of the PanTex is proposed including information on vegetation if available (PanTexG).
The accuracy and robustness of PanTex against seasonal changes, multi-sensor, multi-scene, and data degradation by wavelet-based compression and histogram stretching is discussed with some examples.
Abstract not available
<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: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>
POLYGON ((-180 90, 180 90, 180 -90, -180 -90, -180 90))
|From date||To date|
- Published by
- European Commission, Joint Research Centre
- Created date
- Modified date
- Issued date
- Landing page
- Data theme(s)
- Regions and cities, Science and technology
- Update frequency