A Global Human Settlement Layer From Optical HR/VHR RS Data: Concept and First Results

By Martino Pesaresi, Huadong Guo, X. Blaes, D. Ehrlich, S. Ferri, Lionel Gueguen, Matina Halkia, Mayeul Kauffmann, T. Kemper, Linlin Lu and 6 more. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013

431 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/c575f0b47bad1d0cad4bab605cc5997b307ac4f0

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Citations in the corpus

Mapping Local Climate Zones for a Worldwide Database of the Form and Function of Cities (2015, background): “The alpha version of the global human settlement layer, another global product derived from Landsat data, is now available for four periods between 1975 and 2014 [10].”
The spatial allocation of population: a review of large-scale gridded population data products and their fitness for use (2019, methodology): “…of remotely sensed information about topography, vegetation and land cover has been critical to improving the identification of such places that are potentially inhabited and even the estimation of counts of people living there (Frye et al., 2018; Nieves et al., 2017; Pesaresi et al., 2013).”
Projecting global urban land expansion and heat island intensification through 2050 (2019, methodology): “To build this model, we use four sources of data: urban land extent from the Global Human Settlement Layer (Pesaresi et al 2013), urban population from theUnitedNations (UNDESA2014), GDP from theWorld Bank (World BankGroup 2019), and future scenarios of the SSP from the International Institute for…”
Earth observation as a tool for tracking progress towards the Aichi Biodiversity Targets (2015, neutral): “There are also static products with potential for future change analysis, for example two new global urban settlement layers, the Global Human Settlement Layer (Pesaresi et al. 2013) based on optical imagery and the radar-based Global Urban Footprint (Esch et al. 2014).”
Managing the middle: A shift in conservation priorities based on the global human modification gradient (2019, methodology): “For human settlement, we used population density from the 2015 UN‐adjusted, Gridded Population of the World dataset (Doxsey‐Whitfield et al., 2015), and built‐up areas from the Global Human Settlements Layer (Pesaresi et al., 2013).”
Deep Learning-Based Change Detection in Remote Sensing Images: A Review (2022, background): “Buildings, for example, have complex appearances due to various roof superstructures, such as pipelines, chimneys, and water tanks; as a result, spectral characteristics in VHR images are signiﬁcantly heterogeneous [89,90].”
Slums from Space - 15 Years of Slum Mapping Using Remote Sensing (2016, methodology): “905), specifically the Global Human Settlement Layer (GHSL) (using optical data) and the Global Urban Footprint (GUF) layer (using SAR data) [50,51], stressed the difficulties of detecting the more organic patterns of slums [52].”
Multi-Model Projections of River Flood Risk in Europe under Global Warming (2018, methodology): “Conversely, ISIMIP and JRC-GL made use of global datasets, namely population density from the Global Human Settlement Layer [34], and land use derived from the GlobCover 2009 at 10 s (∼300 m) resolution [35].”