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

Spatial Permeability Model at the European Union Land Border

In the frame of the ISFEREA action, the JRC IPSC carried out an analysis of the permeability of the EU Eastern land border to a specific class of illegal migration flows. This analysis is based on a preliminary version of a spatial quantitative model of the "green border" permeability. This model was built on the assumption that the flow of illegal migrants is function of the geographical permeability of the border and crossing points, the efficacy to control them by the Authorities, and also function of the driving force defined by people's willingness to cross (push and pull factors). The spatial model developed by the JRC attempts to represent in deep only the first part of this equation, while the modularity of the proposed model may allows further development for a more comprehensive explanation the illegal migration phenomena. With the present release the model uses as input more than 20 spatial datasets ranging from satellite remotely-sensed data, land use land cover, digital terrain model, weather and environmental conditions, presence of population, infrastructures, and physical obstacles, and presence of border control points. The model is focused on the geographic permeability related to a standard adult person having illegal behavior and deciding to cross the green border by foot. The basic criteria implemented in the current release are built around three concepts: the rapidity of walking allowed by the terrain and the weather conditions (walk), the possibility to hide by the physical environment (hide), and the probability to be stopped by a border police agent (secure). The geographic permeability is conceptualized as function inverse of the friction surface calculated using fuzzy multi-criteria methodology with a spatial resolution of 1 kilometer. Friction statistics are related to specific spatial contexts around the border lines (from 1 to 50 km) aggregated at the national and sub-national level between different countries. The model confirms the two major entry routes into Europe: the Ukrainian border and the Turkish section of the Greek border. The model shows also the high sensitivity of the potential corridor of Norway but the high number of border points at this boundary seems to be sufficient to protect this entry point. The report concludes that, in spite of limitations linked to data collection and availability, the permeability maps which resulted from this study show the high potential of such a model for the analysis of potential and actual migration flows and related policy planning. This tool could provide an opportunity to test different “what-if” scenarios about the driving forces of illegal migratory flows at the external EU land border, to prioritize investments of Member States in border management infrastructures at EU level, and to develop policy advice for relevant internal and external EU policies. This modeling tool can help estimate the impact on permeability of concrete measures, such as changes in the number of border points or number of border guards and in their level of equipment at these particular points. The flexibility of the model also allows for the input of additional data like typology of border points, in order to improve the results. Finally, the development of a more comprehensive model would require the co-operation of and contribution from relevant authorities of the EU Member States as well as FRONTEX.JRC.G.2-Global security and crisis managemen

Human settlements in low lying coastal zones and rugged terrain: data and methodologies

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 Managemen

Rubble detection from VHR aerial imagery data using differential morphological profiles

Rubble detection is a key element in post disaster crisis assessment and response procedures. In this paper we present an automated method for rapid detection and quantification of rubble from very high resolution (VHR) aerial imagery of urban regions. It is a two step procedure in which the input image is projected on to a hierarchical representation structure for efficient mining and decomposition. Image features matching the geometric and chromatic properties of rubble are fused into a rubble layer that can be re-adjusted interactively. The targeted objects are evaluated based on a density metric given by spatial aggregation. The method is tested on a small-scale exercise on the publically available aerial imagery of Port-au-Prince, Haiti. Performance and preliminary results are discussed.JRC.G.2-Global security and crisis managemen

Global spatial and temporal analysis of human settlements from Optical Earth Observation: Concepts, procedures, and preliminary results

This report provides an overview on the concepts, processing procedures and examples used to quantify changes in built-up land from optical satellite imagery. This is part of the larger work of the Global Human Settlement (GHS) team from the Joint Research Centre (JRC) that aims to measure the spatial extent of global human settlements, to monitor its changes over time and characterize the morphology of settlements. This built-up change analysis addresses the quantification of urbanization including some socio-economic and physical processes associated with urbanization. This includes the quantification of the building stock for modeling physical exposure in disaster risk modeling, as background layer for emergency response when a disaster unfolds and as background building stock layer for normalizing physical loss data. Based on the application of three of the most used change detection methods, Principal Component Analysis, Image Differencing Comparison, and Post-Classification Comparison, we present and discuss preliminary results, and try to identify future research directions for developing an appropriate approach for GHSL result images. The case studies were carried on Alger and Dublin city areas.JRC.G.2-Global security and crisis managemen

DUG User Guide. Version 2.1

This user guide accompanies the DUG tool which is a public tool for applying the “Degree of urbanisation” (DEGURBA) model at one kilometer grid. DUG stands for Degree of Urbanisation Grid. It has been developed in the frame of the “Global Human Settlement Layer” (GHSL) project of the European Commission’s Joint Research Centre, with the overall objective to support the DEGURBA activities. The tool builds on the GHS SMOD model that implements settlement model classifier at 1 km grid. The tool uses population and built-up grids as input data, and optionally a water mask. It has been developed and tested using GHS P2016 datasets ; however other grids can be used on user responsibility. This user guide is a comprehensive guide to all aspects of using the DUG tool. It includes instructions for the set-up of the software, the use of the tool and the manipulation of the data. It presents briefly the basic principles and background information on the methodology and its implementation. Some guidelines on the parametrization are also provided.JRC.E.1-Disaster Risk Managemen

Formal quality assessment of Crisis Maps produced during 2005-2010 - Preliminary results and a proposal for rapid and cost-effective quality assessment

In the last decade, crisis maps have become increasingly a common support in the disaster preparedness and response cycle. In this work, five years of crisis maps from five world leader service providers have been explored and a way to extensively and quickly verify their quality is proposed. A sample of 255 maps has been assessed according to a checklist designed. The clarity of the content, the readability and usability of the maps and the respect of main cartographic standards have been assessed. The first analysis presented in this document highlighted that the basic characteristics expected in good maps are not always respected. The aim of showing current shortcomings in the crisis maps to the scientific community is to foster the improvement of their quality in the future.JRC.DG.G.2-Global security and crisis managemen

Towards an automated monitoring of human settlements in South Africa using high resolution SPOT satellite imagery

Urban areas in sub-Saharan Africa are growing at an unprecedented pace. Much of this growth is taking place in informal settlements. In South Africa more than 10% of the population live in urban informal settlements. South Africa has established a National Informal Settlement Development Programme (NUSP) to respond to these challenges. This programme is designed to support the National Department of Human Settlement (NDHS) in its implementation of the Upgrading Informal Settlements Programme (UISP) with the objective of eventually upgrading all informal settlements in the country. Currently, the NDHS does not have access to an updated national dataset captured at the same scale using source data that can be used to understand the status of informal settlements in the country. This pilot study is developing a fully automated workflow for the wall-to-wall processing of SPOT-5 satellite imagery of South Africa. The workflow includes an automatic image information extraction based on multiscale textural and morphological image features extraction. The advanced image feature compression and optimization together with innovative learning and classification techniques allow a processing of the SPOT-5 images using the Landsat-based National Land Cover (NLC) of South Africa from the year 2000 as low-resolution thematic reference layers as. The workflow was tested on 42 SPOT scenes based on a stratified sampling. The derived building information was validated against a visually interpreted building point data set and produced an accuracy of 97 per cent. Given this positive result, is planned to process the most recent wall-to-wall coverage as well as the archived imagery available since 2007 in the near future.JRC.G.2-Global security and crisis managemen

A new spatial database and software layer supporting the JRC image information query (IQ) system

The database is one of the core components of the image information query (IQ) system: all the information involved in the system is stored inside the database. The database is a searchable archive for the data, the processes, and the satellite imagery processing. With the new database structure it is possible to keep track of every image processing executed inside the system, of all parameters involved to obtain a certain result, and of every input and output involved in the processing. Moreover, the database contains a replica of the metadata of the CID Portal's satellite imagery, with additional information useful for the team’s image processing needs. The database structure is automatically generated and supported by a Python software layer which is also starting the processing on the Linux cluster.JRC.G.2-Global security and crisis managemen

MASADA USER GUIDE

This user guide accompanies the MASADA tool which is a public tool for the detection of built-up areas from remote sensing data. MASADA stands for Massive Spatial Automatic Data Analytics. It has been developed in the frame of the “Global Human Settlement Layer” (GHSL) project of the European Commission’s Joint Research Centre, with the overall objective to support the production of settlement layers at regional scale, by processing high and very high resolution satellite imagery. The tool builds on the Symbolic Machine Learning (SML) classifier; a supervised classification method of remotely sensed data which allows extracting built-up information using a coarse resolution settlement map or a land cover information for learning the classifier. The image classification workflow incorporates radiometric, textural and morphological features as inputs for information extraction. Though being originally developed for built-up areas extraction, the SML classifier is a multi-purpose classifier that can be used for general land cover mapping provided there is an appropriate training data set. The tool supports several types of multispectral optical imagery. It includes ready-to-use workflows for specific sensors, but at the same time, it allows the parametrization and customization of the workflow by the user. Currently it includes predefined workflows for SPOT-5, SPOT-6/7, RapidEye and CBERS-4, but it was also tested with various high and very high resolution1 sensors like GeoEye-1, WorldView-2/3, Pléiades and Quickbird.JRC.E.1-Disaster Risk Managemen