The Global Administrative Unit Layers (GAUL) is an initiative implemented by FAO within the Bill & Melinda Gates Foundation, Agricultural Market Information System (AMIS) and AfricaFertilizer.org projects. The GAUL compiles and disseminates the best available information on administrative units for all the countries in the world, providing a contribution to the standardization of the spatial dataset representing administrative units. The GAUL always maintains global layers with a unified coding system at country, first (e.g. departments) and second administrative levels (e.g. districts). Where data is available, it provides layers on a country by country basis down to third, fourth and lowers levels. The overall methodology consists in a) collecting the best available data from most reliable sources, b) establishing validation periods of the geographic features (when possible), c) adding selected data to the global layer based on the last country boundaries map provided by the UN Cartographic Unit (UNCS), d) generating codes using GAUL Coding System and e) distribute data to the users (see TechnicalaspectsGAUL2015.pdf). Because GAUL works at global level, unsettled territories are reported. The approach of GAUL is to deal with these areas in such a way to preserve national integrity for all disputing countries (see TechnicalaspectsGAUL2015.pdf and G2015_DisputedAreas.dbf). GAUL is released once a year and the target beneficiary of GAUL data is the UN community and other authorized international and national partners. Data might not be officially validated by authoritative national sources and cannot be distributed to the general public. A disclaimer should always accompany any use of GAUL data. 5 territories have been updated respect to the previous release. Moreover, the coastline of American countries or other special areas have been updated using Open Street Map (see ReleaseNoteGAUL2015.pdf). GAUL keeps track of administrative units that has been changed, added or dismissed in the past for political causes. Changes implemented in different years are recorded in GAUL on different layers. For this reason the GAUL product is not a single layer but a group of layers, named "GAUL Set" (see ReleaseNoteGAUL2015.pdf). GAUL 2015 is the eighth release of the GAUL Set.

2010 estimates of total number of people per grid square across Africa South America and Asia, with national totals adjusted to match UN population division estimates, 2012 revision (http://esa.un.org/wpp/)

The GHSL Landsat is a spatial raster dataset that is mapping human settlements globally based on the Landsat satellite data collection. The GHSL Landsat uses the Global Land Survey (GLS) collection of Landsat imagery, which is a carefully coordinated collection of high resolution imagery for global modelling and is produced by the Global Land Cover Facility (www.landcover.org). This allows the mapping of settlements back in time until the year 1975. In addition, Landsat GHSL uses recent Landsat-8 from 2013/2014 for the latest coverage. The aggregated set has the coordinate Reference System: Spherical Mercator (EPSG:3857) and the spatial resolution of 38 m. This data is provided as single GEOTIFF file.

The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1) consists of estimates of human population for the years 1990, 1995, and 2000 by 30 arc-second (1km) grid cells and associated data sets dated circa 2000. A proportional allocation gridding algorithm, utilizing more than 1,000,000 national and sub-national geographic units, is used to assign population values (counts, in persons) to grid cells. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional de Agricultura Tropical (CIAT).

The GHSL Landsat is a spatial raster dataset that is mapping human settlements globally based on the Landsat satellite data collection. The GHSL Landsat uses the Global Land Survey (GLS) collection of Landsat imagery, which is a carefully coordinated collection of high resolution imagery for global modelling and is produced by the Global Land Cover Facility (www.landcover.org). This allows the mapping of settlements back in time until the year 1975. In addition, Landsat GHSL uses recent Landsat-8 from 2013/2014 for the latest coverage. GHS BUILT-UP GRID These data contain a multitemporal information layer on built-up presence as derived from Landsat image collections (GLS1975, GLS1990, GLS2000, and ad-hoc Landsat 8 collection 2013/2014). The data have been produced by means of Global Human Settlement Layer methodology in 2015. 250m of resolution - World Mollweide (EPSG:54009)

World Urban Areas represents the major urban areas of the world as shaded polygons.

2010 estimates of total number of people per grid square across Africa South America and Asia, with national totals adjusted to match UN population division estimates, 2012 revision (http://esa.un.org/wpp/)

A global map of built-up presence derived from backscattered information of Sentinel1 images. Both the GHS BUILT-UP GRID (LDS) as derived from Landsat image collections and the GlobeLand30 (GLC30) were used for training of the Symbolic Machine Learning (SML) classifier. 20m of resolution - Spherical Mercator (EPSG:3857) Dataset name (size): GHS_BUILT_S12016NODSM_GLOBE_R2016A_3857_20 Legend: 0 = no built-up 1 = built-up

The developed approach outputs a global raster layer representing both the spatial distribution and density of built-up areas, for the year 2010. The information about the presence of built-up is expressed as the percentage of built-up area respect to the total surface of the cell. Values are expressed in the range [0 to 100]. The layer is made available as a grid having a spatial resolution of 30-arc seconds (approximately 1 km at the equator), in the WGS84 coordinate system. Being available as a quantitative, continuous raster dataset significantly increases its value by facilitating integration with other spatial datasets for analysis or modeling The method uses machine learning techniques to understand the best population thresholds translating population densities to built-up densities. In the proposed methodology the MODIS Urban Land Cover (ULC) 500 m (C5) made by satellite data of the year circa 2001-2002 is used as training set for classification of the LandScan 2010 Global Population Database (LS). Similar techniques are described in Pesaresi et al. (2013) and Gueguen (2014) for the purpose of finding best rescaling parameters translating remote sensing image-derived features to a high-level-abstraction semantic as “built-up areas”.

World Cities provides a base map layer of the cities for the world. The cities include national capitals, provincial capitals, major population centers, and landmark cities.