Advances on the Global Human Settlement Layer by joint assessment of Earth Observation and population survey data
Martino Pesaresi, Marcello Schiavina, Panagiotis Politis, Sérgio Freire, Katarzyna Goch, Johannes 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
Abstract
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 GHSL’s key components, 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. IoU 0.98 water class, 0.92 built-up class, 0.8 non-residential class, 6% MAE for the 100 m built-up surface or 2.27 m MAE for the building height, 83% TAA for resident population). The paper consolidates GHSL’s theoretical foundation and highlights its innovative features for transparent Artificial Intelligence, facilitating international decision-making processes.