Advances on the Global Human Settlement Layer by joint assessment of Earth Observation and population survey data

<p>The Global Human Settlement Layer (GHSL) project fosters an enhanced, public understanding of the human presence on Earth. A decade after its inception in the Digital Earth 2020 vision, GHSL is an established project of the European Commission’s Joint Research Centre and an integral part of the Copernicus Emergency Management Service. The 2023 GHSL edition, a result of rigorous research on Earth Observation data and population censuses, contributes significantly to understanding worldwide human settlements. It introduces new elements like 10-m-resolution, sub-pixel estimation of built-up surfaces, global building height and volume estimates, and a classification of residential and non-residential areas, improving population density grids. This paper evaluates the key components of the GHSL, including the Symbolic Machine Learning approach, using novel reference data. These data enable a comparative assessment of GHSL model predictions on the evolution of built-up surface, building heights, and resident population. Empirical evidence suggests that GHSL estimates are the most accurate in the public domain today, e.g. achieving an IoU of 0.98 for the water class, 0.92 for the built-up class, and 0.8 for the non-residential class at 10 m resolution. At 100 m resolution, we find that the MAE of built-up surface estimates corresponds to 6% of the grid cell area, the MAE for the building height estimates is 2.27 m, and we find a total allocation accuracy of 83% for resident population. This paper consolidates the theoretical foundation of the GHSL and highlights its innovative features for transparent Artificial Intelligence, facilitating international decision-making processes.</p>

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Publication Details

Journal
International Journal of Digital Earth
Published
2024-08-30
DOI
https://doi.org/10.1080/17538947.2024.2390454
Citations
154
Primary Topic
Impact of Light on Environment and Health
Type
article
Field-Weighted Citation Impact
43.24
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article

Advances on the Global Human Settlement Layer by joint assessment of Earth Observation and population survey data

Panagiotis Politis, Hannah K. Friedrich, Luca Maffenini, Alessandra Carioli et al.
154 citations
International Journal of Digital Earth
Impact of Light on Environment and Health
43.24
article

Advances on the Global Human Settlement Layer by joint assessment of Earth Observation and population survey data

Panagiotis Politis, Hannah K. Friedrich, Luca Maffenini, Alessandra Carioli, Johannes Uhl, Thomas Kemper, Linlin Lu, Christina Corbane, Jamon Van Den Hoek, Sérgio Freire, Jing Gao, Michele Melchiorri, Marcello Schiavina, Vasileios Syrris, Lewis Dijkstra, Stefan Leyk, Martino Pesaresi, Pietro Florio, Katarzyna Krasnodębska, Ines Mari-Rivero
article en
154 citations

Abstract

<p>The Global Human Settlement Layer (GHSL) project fosters an enhanced, public understanding of the human presence on Earth. A decade after its inception in the Digital Earth 2020 vision, GHSL is an established project of the European Commission’s Joint Research Centre and an integral part of the Copernicus Emergency Management Service. The 2023 GHSL edition, a result of rigorous research on Earth Observation data and population censuses, contributes significantly to understanding worldwide human settlements. It introduces new elements like 10-m-resolution, sub-pixel estimation of built-up surfaces, global building height and volume estimates, and a classification of residential and non-residential areas, improving population density grids. This paper evaluates the key components of the GHSL, including the Symbolic Machine Learning approach, using novel reference data. These data enable a comparative assessment of GHSL model predictions on the evolution of built-up surface, building heights, and resident population. Empirical evidence suggests that GHSL estimates are the most accurate in the public domain today, e.g. achieving an IoU of 0.98 for the water class, 0.92 for the built-up class, and 0.8 for the non-residential class at 10 m resolution. At 100 m resolution, we find that the MAE of built-up surface estimates corresponds to 6% of the grid cell area, the MAE for the building height estimates is 2.27 m, and we find a total allocation accuracy of 83% for resident population. This paper consolidates the theoretical foundation of the GHSL and highlights its innovative features for transparent Artificial Intelligence, facilitating international decision-making processes.</p>

International Journal of Digital EarthVol. 17(1)
Oregon State University (US), University of Arizona (US), University of Luxembourg (LU), University of Colorado Boulder (US), Chinese Academy of Sciences (CN), Statistics Belgium (BE), Beijing Institute of Big Data Research (CN), Fincons Group (Italy) (IT), Institute of Geography and Spatial Organization, Polish Academy of Sciences (PL), Joint Research Centre (IT), Aerospace Information Research Institute (CN), University of Delaware (US)
Climate action
Openalex Percentile: Top 0%
Impact of Light on Environment and Health
43.24
DOIOpen Access PDFOpenAlex
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