## 2 Materials and Methods
### The GHSL Data Layers  
Since 2014, various pre-releases of the GHSL data were shared among the GEO partnership. The first public release of the GHSL was announced at the Habitat III conference of Quito, in October 2016. The data are freely accessible from the JRC Open Data Portal ([[https://data.jrc.ec.europa.eu/](https://data.jrc.ec.europa.eu/)]([https://data.jrc.ec.europa.eu/](https://data.jrc.ec.europa.eu/))) and the GEOSS portal ([[http://www.geoportal.org](http://www.geoportal.org)]([http://www.geoportal.org](http://www.geoportal.org))). Since 2017, the GHSL data and tools have been contributing to the GEO Human Planet Initiative, supporting the GEO Strategic Plan 2016-2025. The GHSL suite contains three main grid-based layers (Table 1) covering four epochs: 1975-1990-2000-2015. GHS-BUILT [41] (Figure 1a) is an EO derived product, mapping built-up areas (density). The information is extracted from 40 years of Landsat imagery archives [42] through Symbolic Machine Learning workflows [25; 33; 43]. The process is based on a training set owning heterogeneous completeness (geographical and temporal coverage), thematic definitions, and reliability. The training set includes MERIS Globe Cover [44], LandScan population grids [45], Open Street Map, GeoNames, and MODIS 500 [46]. The result is a series of global maps of built-up areas in grid format for the four epochs in World Mollweide projection (EPSG: 54009):  
* at 250 m resolution in which values are expressed as decimals from 0 to 1 (density);
* at 1 km resolution in which values are expressed as decimals from 0 to 1 (density); and,
* at 30 m resolution in Spherical Mercator (EPSG: 3857), a multi-temporal layer where the presence of built-up areas per epoch is classified in numbers ranging from 6 (built-up area mapped 1975) to 3 (built-up area mapped in 2015), with additional classes for the non-built-up land (2), presence of water (1), and no data (0). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name** & **Semantic** & **Grid Resolution** & **Epoch 1** & **Main Input Data** \\ \hline
**GHS-BUILT** & Density of built-up area & 30 m, 250 m, 1 km & 2015, & Satellite imagery \\  & per grid cell & 2000, & 2000, & Census data, \\
**GHS-POP** & Population counts per grid cell & 250 m, 1 km & 1990, & GHS-BUILT \\  & Classification of each grid & & 1975 & \\  & cell into one of the & & & \\
**GHS-SMOD** & Settlement Model classes: & 1-km & & GHS-BUILT, \\  & high density cluster, low & & & GHS-POP \\  & density cluster, and rural & & & \\  & cells & & & \\ \hline \hline \end{tabular} \({}^{1}\) Temporal dimension.  
\end{table} 
Table 1: Synthesis and features of Global Human Settlement Layer (GHSL) data. GHS-POP [47] maps the estimated resident population on the basis of built-up area presence (Figure 1b). They are based on CIESIN GPWv4 demographic data (derived from census or administrative units), and population is distributed using GHS-BUILT. GHS-POP is available at two resolutions for the four epochs in World Mollweide (EPSG: 54009):  
* at 250 m resolution; and,
* at 1 km resolution. In both layers, the value of each grid cell reports the absolute number of inhabitants in the cell (density, as float). GHS-SMOD (Figure 1c) [48] combines GHS-BUILT and GHS-POP to represent the _Degree of Urbanisation_[49] in the GHSL environment. In the GHS-SMOD, grid cells are classified in three classes: _Urbnan Centre, Urban Cluster_, and _Rural Area_, according to population density and population thresholds (of individual cells and group of cells) [50]. GHS-SMOD is available at 1 km resolution for the four epochs, in World Mollweide (EPSG: 54009). In the layer, cells are classified as rural cells (1), urban cluster (2), and urban centers (3). Urban centers are the human settlements with more than 50,000 inhabitants. Urban centers are delineated according to the _Degree of Urbanisation_ methodology--as group of cells of 1 km with population density (1,500 inhabitants per km\({}^{2}\)), population size (50,000 people), and 50% built-up areas density.