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Human settlements – the places where people live and work – are rapidly evolving. Some of the processes driving the physical changes in human settlements are population and economic growth, rural-u...

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 q...

The Global Human Settlement Layer (GHSL) produces new global spatial information, evidence-based analytics and knowledge describing the human presence on the planet Earth. The GHSL operates in a fully open and free data and methods access policy, building the knowledge supporting the definition, the public discussion and the implementation of European policies and the international frameworks as the 2030 Development Agenda and the related thematic agreements. The GHSL supports the GEO Human Planet Initiative (HPI) that is committed to developing a new generation of measurements and information products providing new scientific evidence and a comprehensive understanding of the human presence on the planet and that can support global policy processes with agreed, actionable and goal-driven metrics. The Human Planet Initiative relies on a core set of partners committed in coordinating the production of the global settlement spatial baseline data. One of the core partners is the European Commission, Directorate General Joint Research Centre, Global Human Settlement Layer project. The Global Human Settlement Layer project produces global spatial information, evidence-based analytics, and knowledge describing the human presence in the planet. This document describes the public release of the GHSL Data Package 2019 (GHS P2019)., JRC.E.1-Disaster Risk Management

The Global Human Settlement Layer Urban Centres Database (GHS-UCDB) is the most complete database on cities to date, publicly released as an open and free dataset - GHS STAT UCDB2015MT GLOBE R2019A V1.0. The database represents the global status on Urban Centres in 2015 by offering cities location, their extent (surface, shape), and describing each city with a set of geographical, socio-economic and environmental attributes, many of them going back 25 or even 40 years in time. Urban Centres are defined in a consistent way across geographical locations and over time, applying the “Global Definition of Cities and Settlements” developed by the European Union to the Global Human Settlement Layer Built-up (GHS-BUILT) areas and Population (GHS-POP) grids. This report contains the description of the dimensions and the derived attributes that characterise the Urban Centres in the database. The document includes notes about methodology and sources. The GHS-UCDB contains information for more than 10,000 Urban Centres and it is the baseline data of the analytical results presented in the Atlas of the Human Planet 2018., JRC.E.1-Disaster Risk Management

The Atlas of the Human Planet 2019 presents key human settlements and urbanisation statistics for 239 countries based on the progress made towards the development of a people-based global harmonised definition of cities and rural areas. Figures and statistics presented in the Atlas 2019 are the result of massive automatic big data processing carried out at the European Commission Directorate General Joint Research Centre in the framework of the Global Human Settlement Layer (GHSL) combining satellite imagery and census information to map settlements, understand their characteristics, and report about their changes over 40 years’ time (1975 – 2015). The Atlas explains the fundamentals of the GHSL, and the service it provides to upscale to the globe the Degree of Urbanisation method (currently adopted as European Union Regulation). Based on the global application of the method the Atlas presents a global urbanisation brief, a commented series of highlights on global human settlement development trajectories, supported by 239 urbanisation briefs, which form the knowledge base for the next generation of urban and territorial policy, development and cooperation action, and global reporting on progress made towards meeting the Sustainable Development Goals and the 2030 Development Agenda as a whole., JRC.E.1-Disaster Risk Management

The combination of Earth Observation and population data produces new information that describes inequalities across the globe in an original, objective and spatially distinct way.The new information contributes to a better understanding of the spatial distribution of wealth and poverty around the globe.The approach has potential for the monitoring and detection of changes in spatial patterns of inequality., JRC.E.1-Disaster Risk Management

The GEO Human Planet Initiative is committed to developing a new generation of measurements and information products that provide new scientific evidence and a comprehensive understanding of the human presence on the planet and that can support global policy processes with agreed, actionable and goal-driven metrics. The Human Planet Initiative relies on a core set of partners committed in coordinating the production of the global settlement spatial baseline data, and an enlarged community of partners developing experimental activities on using the new baseline data for derived post-2015 indicators. One of the core partners is the European Commission, Directorate General Joint Research Centre, Global Human Settlement Layer project. The Global Human Settlement Layer project produces global spatial information, evidence-based analytics, and knowledge describing the human presence in the planet. This document describes the Community pre-Release of the GHSL Data Package 2018 - GHS CR2018 - created for the members of the GEO Human Planet Initiative. The data in this data package have the purpose to gather feedbacks from the GEO Human Planet Initiative community, in preparation for the public release of these data. Disclaimer: the data included in the Community Pre-Release of the GHSL Data Package 2018 should be considered preliminary and not yet validated. It may differ substantially with the data that will be included in the public final Release 2018. The JRC data are provided "as is" and "as available" in conformity with the JRC Data Policy1 and the Commission Decision on reuse of Commission documents (2011/833/EU). Although the JRC guarantees its best effort in assuring quality when publishing these data, it provides them without any warranty of warranty of any kind, either express or implied, including, but not limited to, any implied warranty against infringement of third parties' property rights, or merchantability, integration, satisfactory quality and fitness for a particular purpose. The JRC has no obligation to provide technical support or remedies for the data. The JRC does not represent or warrant that the data will be error free or uninterrupted, or that all non-conformities can or will be corrected, or that any data are accurate or complete, or that they are of a satisfactory technical or scientific quality. The JRC or as the case may be the European Commission shall not be held liable for any direct or indirect, incidental, consequential or other damages, including but not limited to the loss of data, loss of profits, or any other financial loss arising from the use of the JRC data, or inability to use them, even if the JRC is notified of the possibility of such damages., JRC.E.1-Disaster Risk Management

Spatial inequalities across the globe are not easy to detect and satellite data have shown to be of use in this task. Earth Observation (EO) data combined with other information sources can provide complementary information to those derived from traditional methods. This research shows patterns of inequalities emerging by combining global night lights measured from Earth Observation, population density and built-up in 2015. The focus of the paper is to describe the spatial patterns that emerge by combing the three variables. This work focuses on processing EO data to derive information products, and in combining built-up- and population density with nighttime emission. The built-up surface was derived entirely from remote sensing archives using artificial intelligence and pattern recognition techniques. The built-up was combined with population census data to derive population density. Also the nighttime emission data were available from EO satellite sensors. The three layers are subsequently combined as three colour compositions based on the three primary colours (i.e. red green and blue) to display the “human settlement spatial pattern” maps. These GHSL nightlights provide insights in inequalities across the globe. Many patterns seem to be associated with countries income. Typically, high income countries are very well lit at night, low income countries are poorly lit at night. All larger cities of the world are lit at night, those in low-income countries are often less well lit than cites in high-income countries. There are also important differences in nightlights emission in conflict areas, or along borders of countries. This report provides a selected number of patterns that are described at the regional, national and local scale. However, in depth analysis would be required to assess more precisely that relation between wealth access to energy and countries GDP, for example. This work also addresses regional inequality in GHSL nightlights in Slovakia. The country was selected to address the deprivation of the Roma minority community. The work aims to relate the information from the GHSL nightlights with that collected from field survey and census information conducted at the national level. Socio-economic data available at subnational level was correlated with nightlight. The analysis shows that despite the potential of GHSL nightlights in identifying deprived areas, the measurement scale of satellite derived nightlights at 375 x 375 m and 750 x 750 m pixel size is too coarse to capture the inequalities of deprived communities that occur at finer scale. In addition, in the European context the gradient of inequality is not strong enough to produce strong evidence. Although there is a specific pattern of GHSL nightlights in settlements with high Roma presence, this cannot be used to identify such areas among the others. This work is part of the exploratory data analysis conducted within the GHSL team. The exploratory analysis will be followed by more quantitative assessments that will be available in future work., JRC.E.1-Disaster Risk Management

The Atlas of the Human Planet 2018 describes the Urban Centre Database, which was produced in the framework of the Global Human Settlement Layer (GHSL) project by applying a global definition of cities and settlements to the GHSL data. The Atlas presents the key findings of the analysis of geographic, environmental and socio-economic variables that were gathered from free and open sources for each urban centre in the world., JRC.E.1-Disaster Risk Management

People, planet, prosperity and peace are four priorities shared by Africa and Europe, and areas where opportunities for beneficial cooperation abound. Over the past three decades, the European Commission’s Joint Research Centre (JRC) has worked with many organisations and institutions across Africa. This report and its accompanying interactive online service ‘Africa StoryMaps’ present the key findings from this collaboration, and set out options the decision-making, research and education communities may consider. The report focuses on the African dimension of the partnership. It explores the opportunities and challenges arising from the fact that Africa has over twice the population of the European Union (EU), is the world’s most youthful continent, has an economy that is growing faster than that of the EU, is almost seven times larger geographically, yet is vulnerable to diverse internal and external stresses. Tell us what you think about this report. Please fill out and return our feedback form (PDF) at https://ec.europa.eu/jrc/en/file/document/africa-report-feedback-form., JRC.D.6-Knowledge for Sustainable Development and Food Security

This document describes the assessment of global terrain data and a procedure to combine terrain data with newly available human settlement data. The aim is to quantify settlements in low-lying coastal zones and in topographically rugged terrain. For terrain data we use the Shuttle Radar Topographic Mission Digital Elevation Model made available at 90m (3 arc sec), for settlement data we use the Global Human Settlement Layer (GHSL) data set released in 2016 composed of built-up area (GHS-BU), population (GHS-POP) and settlement model (GHS-SMOD) grids and available for 4 epochs, 1975, 1990, 2000 and 2015. We show that SRTM at 90m and GHSL can be combined in a meaningful way. However, we could not generate accuracy assessment on the resulting figures as both datasets do not come with accuracy assessment. In addition, as the data extend only up to 60degrees north, the analysis is not completely global even if it covers the large part of the populated land masses. Preliminary results show that it is possible to derive quantitative measures related to the increase of population in coastal zones, and in steep terrain that may be considered prone to natural hazards. Preliminary analysis indicates that the rate of population growth for the four epochs in the low-lying coastal areas is higher than the global population growth rate. In addition, we show that we are able to measure the spatial expansion of settlements over steep slopes especially in the large cities in developing countries (i.e. Lima), but also in coastal settlements of developed countries (e.g., Italy and France)., JRC.E.1-Disaster Risk Management

This report provides guidance to Member States in improving the coherence and completeness of the national disaster damage and loss data recording process, necessary for supporting evidence-based disaster risk management policies and actions. It proposes essential elements of an assessment methodology for recording damage and loss data and recommends simplified aggregate figures for sharing the data following a common data exchange format. The proposed common framework for damage and loss data recording directly supports reporting on indicators for global disaster risk reduction targets, envisaged as part of the EU commitment to the post-2015 Sustainable Development Goals and to the Sendai Framework for Disaster Risk Reduction. The guidance provided herein is a first iteration and is expected to evolve with further consultation and experience from the multiple stakeholders from Member States., JRC.G.2-Global security and crisis management

In the context of disaster risk management, there is a need for detailed, updated and consistent data on the exposure of people and their settlements. Drawing from the latest generation of satellite imagery, a new approach allows to automatically produce, in an effective way, detailed geodatasets quantifying built-up areas and population distribution. This approach is multi-sensor, multi-scale, multi-use, and focuses on automatic retrieval of quantitative information from the imagery. In parallel with large-scale image processing, global reference datasets were developed which improve the representation of smaller settlements and low-density areas, compared to previously-available maps. These outputs are useful at any stage of the disaster management cycle, by improving the spatial assessment of exposure to hazards and thus contributing to advancing global risk and impact analyses. Coupled with sound decision-making, this ultimately contributes to disaster mitigation efforts., JRC.G.2-Global security and crisis management

This report presents a state of the art for recording disaster loss data in European Union member states. It summarizes the contribution of experts from 15 EU Member States. A comparative analysis shows that methodologies for disaster loss data collection and recording in Europe are heterogeneous and that the available loss databases vary in their level of completeness and detail. Also IT systems vary in purpose, complexity and openness. This precludes a reliable and representative aggregation of loss data at the EU level. This report provides recommendations to European Union Member States that would help increase the quality of (shared) loss data within the current policy framework. It is recommended to continue the consultative process in a third phase with the aim of expanding the network of Member States ready to participate, tackling remaining technical issues in the EU framework for disaster loss recording and assisting Member States in implementing better loss data recording processes., JRC.G.2-Global security and crisis management

In a study commissioned by Directorate General Humanitarian Aid and Civil Protection of the European Commission, the Joint Research Centre formulates technical recommendations for a European approach to standardize loss databases. Loss data are useful for the implementation of disaster risk reduction strategies in Europe (from local to national scales) and to help understand disaster loss trends at global level. Taking stock of existing work, the study defines a conceptual framework for the utility of loss data which allows a cost-benefit analysis of implementation scenarios. The framework considered loss accounting, disaster forensics and risk modelling. Depending on the scale (detail of recording) and scope (geographic coverage), technical requirements will be more or less stringent, and costs of implementation will vary accordingly. The technical requirements proposed in this study rely as much as possible on existing standards, best practices and approaches found in literature, international and national organisations and academic institutions. The requirements cover very detailed recording (at asset level) as well as coarse scale recording. Limitations and opportunities of existing EU legislation are considered as the EU context., JRC.G.2-Global security and crisis management

The document summarizes the steps, procedures, information infrastructure used for collecting built up information from satellite imagery. The document is intended as a reference for visual interpreters. The document also summarizes the lessons learned by photo-interpreters for imagery covering Europe., JRC.G.2-Global security and crisis management

A general framework for processing of high and very-high resolution imagery for creating 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 millions of square kilometres of the Earth surface spread over four continents, corresponding to an estimated population of 1.3 billion of 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, by band, by resolution, and eco-regions. Critical points, lessons learned and next steps are highlighted., JRC.G.2-Global security and crisis management

Rapid urbanisation continues to be an issue with one third of the world’s urban population living under poor living conditions in informal settlements or shanty towns. Improving the lives of this population, which is one objective of the United Nations’ Millennium Development Goals, requires knowledge about the areas under concern. This information is currently still collected in intensive field studies. Earth observation data could be an alternative source of information that can support the process of information collection and on longer terms also serves for monitoring the evolution of those areas. Today’s sub-meter resolution satellites provide very detailed information allowing identification of different urban pattern. However, the huge amount of data requires an automatic information extraction to derive relevant information in a fast and consistent way. Reference data is crucial for the assessment of those algorithms but in case of absence of a relevant data set, which is especially the case in developing countries, an alternative database needs to be defined. In this paper we present a robust approach to produce a reference data set with limited field surveys for the city of Harare (Zimbabwe). This data set is defined to serve two objectives: first, quality assessment of the automatically extracted information and second, further analysis to identify built-up pattern. Two reference data sets are defined using systematic and cluster sampling. The building stock of the city is systematically sampled by visual image interpretation of regular grid points covering the entire image area. Cluster sampling in several stages is performed to define a small sample of buildings that are surveyed in the field. The first stage consists of constructing clusters for the entire city area based on building density and height. From each of these clusters, a sample is randomly selected and in each a sample of buildings is finally randomly selected., JRC.G.2-Global security and crisis management