GHS-BUILT-V ARCTIC R2025A – gridded building volume estimates for the Arctic region (1975-2030)

GHS gridded building volume estimates from 1975 to 2030 in 5-year intervals, derived from Sentinel-2, Landsat, and global DEM data. Values are expressed in unsigned integers and report the estimated amount of building volume in cubic meters. The data are published at 100m and 1km resolution in North Pole LAEA Europe spatial reference system (EPSG:3575).

GHS non-residential (NRES) built-up volume grids derived from joint assessment of Sentinel2, Landsat, and global DEM data, for 1975-2030 (5yrs interval). Values are expressed in unsigned integers and report about the predicted amount of built-up cubic meters. The data is published at 100m and 1km resolution in World Mollweide (EPSG:54009).

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.

The conversion of gridded data between two grids and/or spatial reference systems, commonly referred to as “warping”, is a fundamental step in geospatial data processing workflows. This process involves data resampling, which inherently introduces uncertainty. When dealing with stacks of statistical, gridded datasets measuring cell-level densities, consistently enumerated across multiple points in time, it is crucial to employ a volume-preserving method. Such a method should not only preserve changes in observations along the temporal dimension but also maintain the total sums of measured data per point in time, and allowing for different resampling strategies, while minimizing local distortions in the warped data. Conventional raster-based warping tools available in Geographic Information Systems and coding-based geospatial data processing environments lack explicit control over these critical properties. To address this limitation, we propose a novel vector-based method for areal interpolation based on spatial overlay operations. This approach enables lossless resampling of gridded data, which we apply to the gridded built-up surface data from the Global Human Settlement Layer (GHSL) covering the period from 1975 to 2030. As vector-based spatial data operations are computationally expensive, our method leverages a parallel-processing framework, allowing efficient warping of global gridded data cubes. Furthermore, this approach facilitates the provision of statistical data cubes across various spatial reference systems and grid definitions at planetary scale and high spatial resolution, extendible to the use of areal or spatio-temporal interpolation methods. We implemented this method in Python and call it “VectorCubeWarp”.

In recent years, the Arctic region has garnered attention due to its geo-political and strategic importance: as a reserve for natural resources, commercial routes, and climate activism. Understanding human settlements and their evolution across scales and over time is key to informing stakeholders and policy makers on relevant dynamics. However, the cartographic projections used for global geospatial data often poorly represent the Arctic territory as they are not optimized for polar regions. Herein, we describe GHSL-Arctic, an edition of the Global Human Settlement Layer (GHSL), constituting a comprehensive, up-to-date, and accessible tool for understanding and responding to the rapid changes taking place in the Arctic environment, accounting for this shortcoming. The contribution of this article is two-fold: 1) we present and describe the spatio-temporal variables in GHSL-Arctic, measuring both population and settlements at fine grid scale over the past 50 years (1975-2030), and 2) highlight exemplary analyses, based on GHSL-Arctic, revealing recent settlement patterns and trends in the Arctic.

The Global Human Settlement Layer (GHSL) project produces global spatial data and evidence-based information describing the human presence on Earth. It operates in a fully open and free data and methods access policy. The knowledge generated by the GHSL supports the implementation and public discussion of European policies and the monitoring of international frameworks such as the United Nations Sustainable Development Goals. GHSL is the core dataset of the Exposure Mapping Component under the Copernicus Emergency Management Service (CEMS). Moreover, GHSL data support the GEO (Group on Earth Observations) Human Planet Initiative that is committed to develop a new generation of measurements and information products providing new scientific evidence and a comprehensive understanding of human presence on Earth, supporting global policy processes with agreed, actionable and goal-driven metrics. This document describes the public release of the GHSL Arctic Edition 2025 (GHS ARCTIC R2025A), a region-centric edition of the GHSL data for the Arctic, complementing the global data produced by the GHSL. As the Arctic region has recently garnered attention due to its geo-political and strategic importance, understanding human settlements and their evolution across scales and over time is key to informing stakeholders and policy makers on relevant dynamics. However, the cartographic projections used for global geospatial data often poorly represent the Arctic territory as they are not optimized for polar regions. GHSL Arctic aims to account for this shortcoming, constituting a comprehensive, up-to-date, and accessible tool for understanding and responding to the rapid changes taking place in the Arctic environment.

Abstract not available

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.