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Abstract Monitoring sustainable urban development requires comparable geospatial information on cities across several thematic domains. Here we present the first global database combining such information with city extents. The Global Human Settlement Urban Centre Database (GHS-UCDB) is produced by geospatial data integration to characterise more than 10,000 urban centres worldwide. The database is multi-dimensional and multi-temporal, containing 28 variables across five domains and having multitemporal attributes for one or more epochs when the UC are delineated (1975-1990-2000-2015). Delineation of urban centres for the year 2015 is performed via a logic of grid cell population density, population size, and grid cell contiguity defined by the Degree of Urbanisation method. Each of the urban centres has 160 attributes, including a validation assessment. The novel aspects of this database concern the thematic richness and temporal depth of the variables (across geography, socio-economic, environmental, disaster risk reduction, and sustainable development domains) and the type of geo-information provided (location and extent), featuring an overall consistency that allows comparative analyses across locations and time.

Changing climate and human demographics in the world's mountains will have increasingly profound environmental and societal consequences across all elevations. Quantifying current human populations in and near mountains is crucial to ensure that any interventions in these complex social-ecological systems are appropriately resourced, and that valuable ecosystems are effectively protected. However, comprehensive and reproducible analyses on this subject are lacking. Here, we develop and implement an open workflow to quantify the sensitivity of mountain population estimates over recent decades, both globally and for several sets of relevant reporting regions, to alternative input dataset combinations. Relationships between mean population density and several potential environmental covariates are also explored across elevational bands within individual mountain regions (i.e. "sub-mountain range scale"). Globally, mountain population estimates vary greatly-from 0.344 billion (31%) in 2015. A more detailed analysis using one of the population datasets (GHS-POP) revealed that in ∼35% of mountain sub-regions, population increased at least twofold over the 40-year period 1975-2015. The urban proportion of the total mountain population in 2015 ranged from 6% to 39%, depending on the combination of population and urban extent datasets used. At sub-mountain range scale, population density was found to be more strongly associated with climatic than with topographic and protected-area variables, and these relationships appear to have strengthened slightly over time. Such insights may contribute to improved predictions of future mountain population distributions under scenarios of future climatic and demographic change. Overall, our work emphasizes that irrespective of data choices, substantial human populations are likely to be directly affected by-and themselves affect-mountainous environmental and ecological change. It thereby further underlines the urgency with which the multitudinous challenges concerning the interactions between mountain climate and human societies under change must be tackled.

This study assesses the global mountain population, population change over the 1975–2015 time-range, and urbanisation for 2015. The work uses the World Conservation Monitoring Centre (WCMC) definition of mountain areas combined with that of mountain range outlines generated by the Global Mountain Biodiversity Assessment (GMBA). We estimated population change from the Global Human Settlement Layer Population spatial grids, a set of population density layers used to measure human presence and urbanisation on planet Earth. We show that the global mountain population has increased from over 550 million in 1975 to over 1050 million in 2015. The population is concentrated in mountain ranges at low latitudes. The most populated mountain ranges are also the most urbanised and those that grow most. Urbanisation in mountains (66%) is lower than that of lowlands (78%). However, 34% of the population in mountains live in cities, 31% in towns and semi-dense areas, and 35% in rural areas. The urbanisation rate varies considerably across ranges. The assessments of population total, population trends, and urbanisation may be used to address the issue “not to leave mountain people behind” in the sustainable development process and to understand trajectories of change.

The purpose of this study is to investigate the sensitivity of contrast-based textural measurements and morphological characteristics that derive from high-resolution satellite imagery (three-band SPOT-5) when diverse image enhancements techniques are piloted. The general framework of the application is the built-up/nonbuilt-up detection. In the existence of a low-resolution reference layer, we apply supervised learning that indirectly reduces the uncertainty and improves the quality of the reference layer. Based on the new class label assignments, the image histogram is adjusted suitably for the computation of contrast-based textural/morphological features. A case study is presented where we test a mixture of image enhancement operations like linear and decorrelation stretching and assess the performance through ROC analysis against available building footprints. Experimental results demonstrate that spectral band combination is the key factor that conditions the contrast of grayscale images. Contrast adjustment (before or after the band combination and merging) supports considerably the extraction of informative features from a low-contrast image; in case of a well-contrasted image, the improvement is marginal.

A general framework for processing high and very-high resolution imagery in support of a Global Human Settlement Layer (GHSL) is presented together with a discussion on the results of the first operational test of the production workflow. The test involved the mapping of 24.3 million km2 of the Earth surface spread in four continents, corresponding to an estimated population of 1.3 billion people in 2010. The resolution of the input image data ranges from 0.5 to 10 meters, collected by a heterogeneous set of platforms including satellite SPOT (2 and 5), CBERS 2B, RapidEye (2 and 4), WorldView (1 and 2), GeoEye 1, QuickBird 2, Ikonos 2, and airborne sensors. Several imaging modes were tested including panchromatic, multispectral and pan-sharpened images. A new fully automatic image information extraction, generalization and mosaic workflow is presented that is based on multiscale textural and morphological image features extraction. New image feature compression and optimization are introduced, together with new learning and classification techniques allowing for the processing of HR/VHR image data using low-resolution thematic layers as reference. A new systematic approach for quality control and validation allowing global spatial and thematic consistency checking is proposed and applied. The quality of the results are discussed by sensor, band, resolution, and eco-regions. Critical points, lessons learned and next steps are highlighted.

Exposure is reported to be the biggest determinant of disaster risk, it is continuously growing and by monitoring and understanding its variations over time it is possible to address disaster risk reduction, also at the global level. This work uses Earth observation image archives to derive information on human settlements that are used to quantify exposure to five natural hazards. This paper first summarizes the procedure used within the global human settlement layer (GHSL) project to extract global built-up area from 40 year deep Landsat image archive and the procedure to derive global population density by disaggregating population census data over built-up area. Then it combines the global built-up area and the global population density data with five global hazard maps to produce global layers of built-up area and population exposure to each single hazard for the epochs 1975, 1990, 2000, and 2015 to assess changes in exposure to each hazard over 40 years. Results show that more than 35% of the global population in 2015 was potentially exposed to earthquakes (with a return period of 475 years); one billion people are potentially exposed to floods (with a return period of 100 years). In light of the expansion of settlements over time and the changing nature of meteorological and climatological hazards, a repeated acquisition of human settlement information through remote sensing and other data sources is required to update exposure and risk maps, and to better understand disaster risk and define appropriate disaster risk reduction strategies as well as risk management practices. Regular updates and refined spatial information on human settlements are foreseen in the near future with the Copernicus Sentinel Earth observation constellation that will measure the evolving nature of exposure to hazards. These improvements will contribute to more detailed and data-driven understanding of disaster risk as advocated by the Sendai Framework for Disaster Risk Reduction.

In addition, the request for information is also driven by the need to cover data poor regions of low income countries where the coverage of the information is rarely available outside the largest cities (Farrell [15]). In addition, the project recognizes that the EO based information needs to be integrated with statistical demographic and socio-economic records, and generated from new data sources including that of voluntary geographic information and human sensors on the move such as from mobile phones. First, it provides a computing space for image processing specialists to process large data volumes and data streams; second, it provides access of open information products on settlements grouped under specific windows; and, third, it facilitates the dissemination of information and results to potential decision makers. We look forward to future improvements in the extraction of information that will be coming from Big Data Earth Observation (EO) data archives (Corbane et al. [8]) and the incoming open source EO data streams. [Extracted from the article]

The presence of green spaces within city centres has been recognized as a valuable component of the city landscape. Vegetation provides a variety of benefits including energy saving, improved air quality, reduced noise pollution, decreased ambient temperature and psychological restoration. Evidence also shows that the amount of vegetation, known as 'greenness', in densely populated areas, can also be an indicator of the relative wealth of a neighbourhood. The 'grey-green divide', the contrast between built-up areas with a dominant grey colour and green spaces, is taken as a proxy indicator of sustainable management of cities and planning of urban growth. Consistent and continuous assessment of greenness in cities is therefore essential for monitoring progress towards the United Nations Sustainable Development Goal 11. The availability of multi-temporal greenness information from Landsat data archives together with data derived from the city centres database of the Global Human Settlement Layer (GHSL) initiative, offers a unique perspective to quantify and analyse changes in greenness across 10,323 urban centres all around the globe. In this research, we assess differences between greenness within and outside the built-up area for all the urban centres described by the city centres database of the GHSL. We also analyse changes in the amount of green space over time considering changes in the built-up areas in the periods 1990, 2000 and 2014. The results show an overall trend of increased greenness between 1990 and 2014 in most cities. The effect of greening is observed also for most of the 32 world megacities. We conclude that using simple yet effective approaches exploiting open and free global data it is possible to provide quantitative information on the greenness of cities and its changes over time. This information is of direct interest for urban planners and decision-makers to mitigate urban related environmental and social impacts. [ABSTRACT FROM AUTHOR]