The spatial raster dataset depicts the spatial distribution of the building heights as extracted from the filtering of a composite of global digital elevation models (DEM) and the filtering of satellite imagery using linear regression techniques. The used input DEMs are the ALOS World 3D - 30m (AW3D30, 2006-2011) and the Shuttle Radar Topography Mission 30m (SRTM30, 2000). The building heights extracted from these sources are updated using the support of the shadow markers extracted from the Sentinel2 image data composite of the year 2018 (GHS-composite-S2 R2020A). The data is published at 100m resolution in World Mollweide (ESRI:54009). The building height information are summarized to the grid cell as Average of the Gross Building Height (AGBH) and Average of the Net Building Height (ANBH). The AGBH is expressed in terms of cubic meters per surface unit (volumetric built density), while the ANBH is expressed in terms of meters.
- Panagiotis Politis
How to cite
Pesaresi, Martino; Politis, Panagiotis (2023): GHS-BUILT-H R2023A - GHS building height, derived from AW3D30, SRTM30, and Sentinel2 composite (2018). European Commission, Joint Research Centre (JRC) [Dataset] doi: 10.2905/85005901-3A49-48DD-9D19-6261354F56FE PID: http://data.europa.eu/89h/85005901-3a49-48dd-9d19-6261354f56fe
GHS Average of the Gross Building Height (AGBH). Values are expressed as decimals (Float) reporting about the amount of built cubic meters per surface unit. The data are published at 100m resolution in World Mollweide (EPSG:54009). The compressed ZIP file contain TIF files and short documentation.
GHS Average of the Net Building Height (ANBH). Values are expressed as decimals (Float) reporting about the average height of the built surfaces. The data are published at 100m resolution in World Mollweide (EPSG:54009). The compressed ZIP file contain TIF files and short documentation.
- 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.
- PUBLIC LIBRARY SCIENCE, SAN FRANCISCO, USA
The estimation of the vertical components of built-up areas from free Digital Elevation Model (DEM) global data filtered by multi-scale convolutional, morphological and textural transforms are generalized at the resolution of 250 meters using linear least-squares regression techniques. Six test cases were selected: Hong Kong, London, New York, San Francisco, Sao Paulo, and Toronto. Five global DEM and two DEM composites are evaluated in terms of 60 combinations of linear, morphological and textural filtering and different generalization techniques. Four generalized vertical components estimates of built-up areas are introduced: the Average Gross Building Height (AGBH), the Average Net Building Height (ANBH), the Standard Deviation of Gross Building Height (SGBH), and the Standard Deviation of Net Building Height (SNBH). The study shows that the best estimation of the net GVC of built-up areas given by the ANBH and SNBH, always contains a greater error than their corresponding gross GVC estimation given by the AGBH and
SGBH, both in terms of mean and standard deviation. Among the sources evaluated in this study, the best DEM source for estimating the GVC of built-up areas with univariate linear regression techniques is a composite of the 1-arcsec Shuttle Radar Topography Mission (SRTM30)
and the Advanced Land Observing Satellite (ALOS) World 3D–30 m (AW3D30) using the union operator (CMP_SRTM30-AW3D30_U). A multivariate linear model was developed using 16 satellite features extracted from the CMP_SRTM30-AW3D30_U enriched by other land cover sources, to estimate the gross GVC. A RMSE of 2.40 m and 3.25 m was obtained for the AGBH and the SGBH, respectively. A similar multivariate linear model was developed to estimate the net GVC. A RMSE of 6.63 m and 4.38 m was obtained for the ANBH and the SNBH, respectively. The main limiting factors on the use of the available global DEMs for estimation the GVC of built-up areas are two. First, the fact that the horizontal resolution of these sources (circa 30 and 90 meters) corresponds to a sampling size that is larger than the expected average horizontal size of built-up structures as detected from nadir-angle EO data, producing more reliable estimates for gross vertical components than for net vertical component of built-up areas. Second, post-production processing targeting Digital Terrain Model specifications may purposely filter out the information on the vertical component of built-up areas that are contained in the global DEMs.
Under the limitations of the study presented here, these results show a potential for using global DEM sources in order to derive statistically generalized parameters describing the vertical characteristics of built-up areas, at the scale of 250x250 meters. However, estimates need to be evaluated in terms of the specific requirements of target applications such as spatial population modelling, urban morphology, climate studies and so on.
- 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.
Abstract not available
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