Breaking new ground in mapping human settlements from space – The Global Urban Footprint

By Thomas Esch, W. Heldens, Andreas Hirne, M. Keil, Mattia Marconcini, A. Roth, J. Zeidler, S. Dech, Emanuele Strano. 2017

375 citations recorded by the source corpus.

Abstract Today, approximately 7.2 billion people inhabit the Earth and by 2050 this number will have risen to around nine billion, of which about 70% will be living in cities. The population growth and the related global urbanization pose one of the major challenges to a sustainable future. Hence, it is essential to understand drivers, dynamics, and impacts of the human settlements development. A key component in this context is the availability of an up-to-date and spatially consistent map of the location and distribution of human settlements. It is here that the Global Urban Footprint (GUF) raster map can make a valuable contribution. The new global GUF binary settlement mask shows a so far unprecedented spatial resolution of 0.4 ″ ( ∼ 12 m ) that provides – for the first time – a complete picture of the entirety of urban and rural settlements. The GUF has been derived by means of a fully automated processing framework – the Urban Footprint Processor (UFP) – that was used to analyze a global coverage of more than 180,000 TanDEM-X and TerraSAR-X radar images with 3 m ground resolution collected in 2011–2012. The UFP consists of five main technical modules for data management, feature extraction, unsupervised classification, mosaicking and post-editing. Various quality assessment studies to determine the absolute GUF accuracy based on ground truth data on the one hand and the relative accuracies compared to established settlements maps on the other hand, clearly indicate the added value of the new global GUF layer, in particular with respect to the representation of rural settlement patterns. The Kappa coefficient of agreement compared to absolute ground truth data, for instance, shows GUF accuracies which are frequently twice as high as those of established low resolution maps. Generally, the GUF layer achieves an overall absolute accuracy of about 85%, with observed minima around 65% and maxima around 98%. The GUF will be provided open and free for any scientific use in the full resolution and for any non-profit (but also non-scientific) use in a generalized version of 2.8 ″ ( ∼ 84 m ). Therewith, the new GUF layer can be expected to break new ground with respect to the analysis of global urbanization and peri-urbanization patterns, population estimation, vulnerability assessment, or the modeling of diseases and phenomena of global change in general.

Subfields: Global and Planetary Change, Media Technology

View Paper: https://www.semanticscholar.org/paper/2ab3d4d020e8b380c17141c4ba4728c73d5ad255

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Citations in the corpus

The spatial allocation of population: a review of large-scale gridded population data products and their fitness for use (2019, methodology): “The quality of remotely sensed ancillary data also depends heavily on the characteristics of the instrument (optical daytime, optical nighttime or radar) and the processing algorithm (e.g., Small et al., 2005; Potere et al., 2009; Pesaresi et al., 2016b; Esch et al., 2017).”
Tackling Climate Change with Machine Learning (2019, background): “The resolution of infrastructure data ranges from coarse localization of all buildings at the global scale [319], to precise 3D reconstruction of a neighborhood [323].”