Advances on the Global Human Settlement Layer by joint assessment of Earth Observation and population survey data
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 Cope...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | International Journal of Digital Earth |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2024.2390454 |
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| author | Martino Pesaresi Marcello Schiavina Panagiotis Politis Sergio Freire Katarzyna Krasnodębska Johannes H. Uhl Alessandra Carioli Christina Corbane Lewis Dijkstra Pietro Florio Hannah K. Friedrich Jing Gao Stefan Leyk Linlin Lu Luca Maffenini Ines Mari-Rivero Michele Melchiorri Vasileios Syrris Jamon Van Den Hoek Thomas Kemper |
| author_facet | Martino Pesaresi Marcello Schiavina Panagiotis Politis Sergio Freire Katarzyna Krasnodębska Johannes H. Uhl Alessandra Carioli Christina Corbane Lewis Dijkstra Pietro Florio Hannah K. Friedrich Jing Gao Stefan Leyk Linlin Lu Luca Maffenini Ines Mari-Rivero Michele Melchiorri Vasileios Syrris Jamon Van Den Hoek Thomas Kemper |
| author_sort | Martino Pesaresi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5eada1a535274db6a93a898ca8865701 |
| institution | Kabale University |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-5eada1a535274db6a93a898ca88657012024-12-16T09:53:26ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552024-12-0117110.1080/17538947.2024.2390454Advances on the Global Human Settlement Layer by joint assessment of Earth Observation and population survey dataMartino Pesaresi0Marcello Schiavina1Panagiotis Politis2Sergio Freire3Katarzyna Krasnodębska4Johannes H. Uhl5Alessandra Carioli6Christina Corbane7Lewis Dijkstra8Pietro Florio9Hannah K. Friedrich10Jing Gao11Stefan Leyk12Linlin Lu13Luca Maffenini14Ines Mari-Rivero15Michele Melchiorri16Vasileios Syrris17Jamon Van Den Hoek18Thomas Kemper19Directorate for Societal Resilience and Security, European Commission, Joint Research Centre (JRC), Ispra, ItalyNTT DATA, Brussels, BelgiumEuropean Dynamics Belgium S.A., Brussels, BelgiumDirectorate for Societal Resilience and Security, European Commission, Joint Research Centre (JRC), Ispra, ItalyInstitute of Geography and Spatial Organization, Polish Academy of Sciences, Warsaw, PolandDirectorate for Societal Resilience and Security, European Commission, Joint Research Centre (JRC), Ispra, ItalyDirectorate for Societal Resilience and Security, European Commission, Joint Research Centre (JRC), Ispra, ItalyDirectorate for Societal Resilience and Security, European Commission, Joint Research Centre (JRC), Ispra, ItalyDirectorate for Fair and Sustainable Economy, European Commission Joint Research Centre (JRC), Ispra, ItalyDirectorate for Societal Resilience and Security, European Commission, Joint Research Centre (JRC), Ispra, ItalySchool of Geography, Development & Environment, University of Arizona, Tucson, AZ, USADepartment of Geography and Spatial Sciences, & Data Science Institute, University of Delaware, Newark, DE, USADepartment of Geography, University of Colorado Boulder, Boulder, CO, USAInternational Research Center of Big Data for Sustainable Development Goals, Beijing, People’s Republic of ChinaUniSystems Luxembourg Sàrl, Bertrange, LuxembourgFINCONS SPA, Vimercate, ItalyDirectorate for Societal Resilience and Security, European Commission, Joint Research Centre (JRC), Ispra, ItalyEuropean Union Intellectual Property Office (EUIPO), Alicante, SpainCollege of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, OR, USADirectorate for Societal Resilience and Security, European Commission, Joint Research Centre (JRC), Ispra, ItalyThe 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.https://www.tandfonline.com/doi/10.1080/17538947.2024.2390454GHSLgeospatial XAIbuilt-up surfacepopulation gridsdegree of urbanizationbuilding height |
| spellingShingle | Martino Pesaresi Marcello Schiavina Panagiotis Politis Sergio Freire Katarzyna Krasnodębska Johannes H. Uhl Alessandra Carioli Christina Corbane Lewis Dijkstra Pietro Florio Hannah K. Friedrich Jing Gao Stefan Leyk Linlin Lu Luca Maffenini Ines Mari-Rivero Michele Melchiorri Vasileios Syrris Jamon Van Den Hoek Thomas Kemper Advances on the Global Human Settlement Layer by joint assessment of Earth Observation and population survey data International Journal of Digital Earth GHSL geospatial XAI built-up surface population grids degree of urbanization building height |
| title | Advances on the Global Human Settlement Layer by joint assessment of Earth Observation and population survey data |
| title_full | Advances on the Global Human Settlement Layer by joint assessment of Earth Observation and population survey data |
| title_fullStr | Advances on the Global Human Settlement Layer by joint assessment of Earth Observation and population survey data |
| title_full_unstemmed | Advances on the Global Human Settlement Layer by joint assessment of Earth Observation and population survey data |
| title_short | Advances on the Global Human Settlement Layer by joint assessment of Earth Observation and population survey data |
| title_sort | advances on the global human settlement layer by joint assessment of earth observation and population survey data |
| topic | GHSL geospatial XAI built-up surface population grids degree of urbanization building height |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2024.2390454 |
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