GHSL/UA Integration: Feasibility Report. Application of the JRC GHSL Image Information Extraction Protocol in the frame of the Urban Atlas product specifications

JRC started the design of the global human settlement layer (GHSL) concept during 2010-2011, together with the development of an image query (IQ) system able to generate and manage geoinformation in an integrated way. The IQ system aggregated the experiences related to automatic information extraction from meter and sub-metre resolution satellite image data in the disaster and crisis management scenarios supported by JRC since 2003-2004. The first alpha-test of the IQ system was delivered in Dec 2011, performing a GHSL image information query task over high and very-high resolution satellite image data covering more than 615 billions of square kilometres of global earth surface, mostly placed in populated regions of Europe, Africa, Asia and South America. During 2011, first contacts with DGREGIO were made in order to understand if the JRC IQ technology and the derived GHSL information layers may be of interest in the context of the “European Urban Atlas” (UA) implementation and in general, in pan-European mapping and characterization of European settlements. This feasibility report describes the application of the GHSL protocol according to the Urban Atlas product specifications and more specifically the comparison between SSL output information with the GHSL built-up information extraction in the context of the Urban Atlas 2012-2013. The objectives of the work described in this report were i) to test the processing capacity of the JRC IQ system in order to assess the feasibility of a pan-European GHSL coverage or “built-upareas detection” using the image data prepared for the UA 2012-2013, ii) to assess the reliability and added value of the automatic image information retrieval by systematic comparison of the automatic output with a known reference layer reporting about similar information, namely, the European soil sealing layer.JRC.G.2-Global security and crisis managemen

Urban and Regional Built-up Analysis (URBA): Higher resolution (2.5m) of built-up detection GHSL Europe wall-to-wall coverage

Mapping and analyzing settlements in Europe is an on-going research project performed in collaboration with the Directorate-General for Regional and Urban Policy (DG REGIO). The work described in this technical report, summarizes the customization and improvements of the GHSL technology towards a wall-to-wall, gap-free coverage of Europe. The output of this work has been published under the European Environment Agency portal in December 2014 as the European Settlement Map (ESM). This map represents human settlements in Europe at 100m of resolution. The report details the GHSL workflows as they evolved from 2013 to 2014, towards the publication of the European Settlement Map, as well as specific problems in the extraction of information from the SPOT 5 and SPOT 6 satellite imagery; data gaps due to cloud coverage or otherwise missing data, and how these issues were addressed and resolved before the publication of the European Settlement Map.JRC.E.1-Disaster Risk Managemen

Mapping for Multi-Source Visualization: Scientific Information Retrieval Service (SIRS)

This paper introduces SIRS and discusses the design process of a multi-index, multi-source information retrieval system. SIRS provides comprehensive visualization of different document types for the JRC working environment. The interface design is based on elastic window management and on the Focus+Context method to browse large amounts of information without losing its contextual relevance. Source integration was achieved by mapping techniques, on which we applied methods, degree-of-separation and closure, to provide advanced relational context for objects

The Global Conflict Risk Index (GCRI): Regression model, data ingestion, processing and output methods

The GCRI is a quantitative conflict risk model, developed by the JRC and based solely on open source data, providing quantitative input to the EU early warning framework, one input to the EU Conflict Early Warning System (EWS), developed by the European External Action Service (EEAS) in close partnership with the European Commission to enhance the EU's conflict prevention capacities. The GCRI distinguishes between three types of violent conflict a state may experience: civil war over national power, subnational conflicts over secession, autonomy, or resources, and conflicts in the international sphere. While the latter are not currently modelled by GCRI, for the first two the index quantifies the probability and the intensity respectively of national and subnational conflicts occurring in the next one to four years. Relying on historical data and a statistical model that includes political, socio-economic, environmental and security variables, it assesses the level and likelihood of future conflicts The GCRI is composed of two statistical models: the regression model and the composite model. Both models are based on twenty-four individual variables. This report presents the work done between February 2017 and September 2017, specifically focused on improving the documentation on the regression model. The present report describes on the one hand the regression model, including the input data and the model itself. On the other hand, it presents the statistical significance test and the matrix of confusion that have been performed, in order to get a highly detailed analysis of the performances of the model. The results of these analyses are presented in chapter 4 and 5. This report is part of a series of documentations produced in 2017 aiming at improving the GCRI models with greater transparency and robustness. This work contributes to enhancing the GCRI performance.JRC.E.1-Disaster Risk Managemen

Conflict Resilience

Although large-scale wars and interstate conflicts have almost disappeared, intrastate conflicts remain widespread and result in a high number of victims. During the last ten years, the number of fatalities was substantially higher than in the previous decade. Though these conflicts take place outside the borders of the EU, they can generate important direct and indirect effects. Moreover, they are connected to climate change, can lead to various disasters, geopolitical effects, or material supply disruptions. The concept of resilience has recently gained ground as a framework for addressing contemporary global threats. It has also become the key principle in the EU’s external action. One of its key building blocks is the modelling and monitoring of conflict risk to allow early action. Conflict resilience refers to the capacity of a state to resist a drift towards violence contrary to the structural conditions prevailing (pre-conflict resilience). It also includes the response of a state in the presence of a conflict (post-conflict resilience). Evaluating the pre-conflict resilience of states can provide insights into conflict aversion or enable a warning for the eruption of violence. On the other hand, the study of postconflict resilience may unveil the adaptive and transformative mechanisms that can be followed by other war-torn countries. Climate change and conflicts are closely related. For example, climate change exacerbates current conflict drivers like food insecurity, competition for water and land resources, poverty and internal displacement of people. Adaptation and mitigation policies may lead to new regulations or infrastructures (like new hydropower reservoirs) which can generate tensions and eventually conflicts. Finally, conflict-torn countries are unable to invest in adaptation strategies, which makes them even more vulnerable to climate change effects.JRC.E.1-Disaster Risk Managemen

Global Conflict Risk Index: New variables in 2018

The Global Conflict Risk Index (GCRI) is an early warning system designed to give policy makers a global risk assessment based on economic, social, environmental, security and political factors. The GCRI is composed of two statistical models: the regression model, that quantifies the probability and the intensity of national and subnational conflicts occurring in the next one to four years, and the composite model, whose aim is to provide an overview of the factors contributing to conflict at country level. Both models are based on twenty-four individual variables, whose raw data are open-source. While it is generally agreed that political and social variables are the most relevant ones for conflict risk modelling, other variables and their linkages with armed conflicts have received growing attention from both academics and policy makers in recent years, e.g. climate variability. Indeed, the nature of conflict is evolving and the diversity of conflict drivers has been acknowledged. In this report new triggers of instability, such as climate variability, levels of resilience, and displaced people are explored as drivers of conflicts. The aim is to improve the accuracy of the regression model, and further develop the GCRI with new variables.JRC.E.1-Disaster Risk Managemen

The Global Conflict Risk Index: Artificial intelligence for conflict prevention

The Global Conflict Risk Index (GCRI), which was designed by the European Commission’s Joint Research Centre (JRC), is the quantitative starting point of the EU’s conflict Early Warning System. Taking into consideration the needs of policy-makers to prioritize actions towards conflict prevention, the GCRI expresses the statistical risk of violent conflict in a given country in the upcoming one to four years. It is based on open source data and grounded in the assumption that the occurrence of conflict is linked to structural conditions, which are used to compute the probability and intensity of conflicts. While the initial GCRI model was estimated by means of linear and logistic regression models, this report presents a new GCRI model based on the Artificial Intelligence (AI) random forest (RF) approach. The models’ hyperparameters are optimized using a ten-fold cross validation. Overall, it is demonstrated that the random forest GCRI models are internally stable, not overfitting, and have a good predictive power. The precision and accuracy metrics are above 98%, both for the national power and subnational power conflict models. The AI GCRI, as a supplementary modelling method for the European conflict prevention policy agenda, is scientifically robust as a baseline quantitative evaluation of armed conflict risk additional to the linear and logistic regression GCRI.JRC.E.1-Disaster Risk Managemen

Global Crisis Atlas: Mapping for Situational awareness

This report is produced by the Peace & Stability team of the Joint Research Centre, which seats under the umbrella of the Disaster Risk Management Unit (E1) – Directorate Space, Security and Migration. The team deals with projects related to the understanding of crisis and conflict risk dynamics. The ultimate objective is the enhancement of situational awareness and the support to decision-making processes of EU policymakers during critical or potentially critical situations. Global Crisis Atlas (GCA) is one of the work packages of the Peace & Stability team. It consists of a repository of digital maps, accessible by the means of a web interface, that furnish an overview of the elements that influence (or might influence) the rise or unfolding of crises and conflicts. GCA maps are produced to respond to the intelligence requirements of the European External Action Service and to complement the information already provided by its intelligence centre (INTCEN - EU Intelligence and Situation Centre). The current report is divided into four chapters. The first two chapters serve as presentation of the GCA theoretical framework (chapter 1) and of the tool’s features and functionalities (chapter 2). The last two chapters highlight future developments in terms of new functionalities implemented as well as innovative approaches to the understanding of crises and conflicts that the GCA tool may enable.JRC.E.1-Disaster Risk Managemen

Global Human Settlement Analysis for Disaster Risk Reduction

The Global Human Settlement Layer (GHSL) is supported by the European Commission, Joint Research Center (JRC) in the frame of his institutional research activities. Scope of GHSL is developing, testing and applying the technologies and analysis methods integrated in the JRC Global Human Settlement analysis platform for applications in support to global disaster risk reduction initiatives (DRR) and regional analysis in the frame of the European Cohesion policy. GHSL analysis platform uses geo-spatial data, primarily remotely sensed and population. GHSL also cooperates with the Group on Earth Observation on SB-04-Global Urban Observation and Information, and various international partners andWorld Bank and United Nations agencies. Some preliminary results integrating global human settlement information extracted from Landsat data records of the last 40 years and population data are presented.JRC.G.2-Global security and crisis managemen

Operating procedure for the production of the Global Human Settlement Layer from Landsat data of the epochs 1975, 1990, 2000, and 2014

A new global information baseline describing the spatial evolution of the human settlements in the past 40 years is presented. It is the most spatially global detailed data available today dedicated to human settlements, and it shows the greatest temporal depth. The core processing methodology relies on a new supervised classification paradigm based on symbolic machine learning. The information is extracted from Landsat image records organized in four collections corresponding to the epochs 1975, 1990, 2000, and 2014. The experiment reported here is the first known attempt to exploit global Multispectral Scanner data for historical land cover assessment. As primary goal, the Landsat-made Global Human Settlement Layer (GHSL) reports about the presence of built-up areas in the different epochs at the spatial resolution allowed by the Landsat sensor. Preliminary tests confirm that the quality of the information on built-up areas delivered by GHSL is better than other available global information layers extracted by automatic processing from Earth Observation data. An experimental multiple-class land-cover product is also produced from the epoch 2014 collection using low-resolution space-derived products as training set. The classification schema of the settlement distinguishes built-up areas based on vegetation contents and volume of buildings, the latter estimated from integration of SRTM and ASTER-GDEM data. On the overall, the experiment demonstrated a step forward in production of land cover information from global fine-scale satellite data using automatic and reproducible methodology.JRC.G.2-Global security and crisis managemen