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

Validation Protocol for Emergency Response Geo-information Products

Europe is making a significant effort to develop (geo)information services for crisis management as part of the Global Monitoring for Environment and Security GMES) programme. Recognising the importance of coordinated European response to crises and the potential contribution of GMES, the Commission launched a number of preparatory activities in coordination with relevant stakeholders for the establishment of an Emergency Response GMES Core Service (ERCS). GMES Emergency Response Services will rely on information provided by advanced technical and operational capabilities making full use of space earth observation and supporting their integration with other sources of data and information. Data and information generated by these services can be used to enhance emergency preparedness and early reaction to foreseeable or imminent crises and disasters. From a technical point of view, the use of geo-information for emergency response poses significant challenges for spatial data collection, data management, information extraction and communication. The need for an independent formal assessment of crisis products to provide operational services with homogeneous and reliable standards has recently become recognized as an integral component of service development. Validation is intended to help end-users decide how much to trust geo-information products (maps, spatial dataset). The focus, in this document, is on geo-information products, in particular those derived from Earth Observation data. Validation principles have been implemented into a protocol, as a tool to check whether the products meet standards and user needs. The validation principles, methods, rules and guidelines provided in this document aim to give a structure that guarantees an overall documented and continuous quality of ERCS products.JRC.DG.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

MASADA Sentinel 1 & 2 User Guide

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, by automatic classification of 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 first version of MASADA (v1.3) supports Very High Resolution satellite data and includes pre-defined workflows for a variety of sensors (e.g. SPOT-5, SPOT-6/7, RapidEye, CBERS-4). The second version of MASADA (v2.0) is tailored to the processing of Copernicus Sentinel-1 and Sentinel-2 data. Two workflows building on the SML but adapted to the characteristics of each of the two sensors have been implemented in a stand-alone software. The tool is designed for the processing of single scenes, for batch processing of a series of scenes and for parallel processing of large datasets thanks to a dedicated command-line interface. This user guide is a comprehensive guide to all aspects of using the MASADA tool. It includes instructions for the installation 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 two main modules integrated in this new version: S1 module and S2 module. Some guidelines on the parametrization of the modules are also provided together with test datasets.JRC.E.1-Disaster Risk Managemen

Next Generation Mapping of Human Settlements from Copernicus Sentinel-2 data: leveraging cloud computing, machine learning and earth observation data

Since the advent of the openly accessible Sentinel satellite data as part of the Copernicus programme of the European Commission and ESA, massive amounts of satellite data have brought disruptive changes in Earth observation data management and analysis. In the context of the Global Human Settlement Layer activities, the Copernicus Sentinel-2 mission offers new opportunities for mapping human settlements over large areas and for the update and improvement of the Global Human Settlement Layer datasets and information layers. Concurrently, state-of-the-art machine learning algorithms and cloud computing infrastructures have become available with a great potential to revolutionize the image processing of satellite remote sensing. Within this context, this study explores the feasibility of refactoring the existing GHSL workflows and applications into the cloud computing paradigm by leveraging the functionalities offered by the Distributed Web Platform WASDI combined with advanced machine learning methods for image processing and classification. In this report, we summarize the lessons learnt using WASDI for mapping of built-up areas from Sentinel data. We present the advantages of both convenient and powerful workflow management and cloud scalability and the experiences gained and challenges using the WASDI platform. The experiments showed that porting of the GHSL workflows to DIAS can be facilitated by the WASDI interface. When testing two different cloud providers, large differences in the time for accessing the Sentinel-2 data and downloading it were observed and had the largest impact on the performances of the workflows.JRC.E.1-Disaster Risk Managemen

GHSL-S2 plugin User Guide

The GHSL-S2 Tool (version 1.0) is a visualization and download tool (QGIS plugin) developed in the frame of the Global Human Settlement Layer (GHSL) project . It facilitates the access to the GHSL S2 products using a free and open-source cross-platform Geographic Information System software (QGIS v.3.8 or higher). It provides a handy way to explore the GHSL datasets, to classify the probabilistic built-up layer derived from Sentinel-2 image composite and to export user-defined subsets, while avoiding the download of large files. The GHS-S2 tool is developed in Python programming language as a QGIS plugin. It bridges the QGIS software to the Google Earth Engine (GEE) cloud-based platform , making use of the GEE plugin , which integrates GEE and QGIS using the EE Python API . It requires an active GEE account and an internet connection. This document contains the description of the GHS-S2 tool usage, the main features and functionalities. The GHSL-S2 plugin is part of the GHSL tools suite and issued with an end-user licence agreement, included in the download package.JRC.E.1-Disaster Risk Managemen

Atlas of the Human Planet 2017: Global Exposure to Natural Hazards

The Atlas of the Human Planet 2017. Global Exposure to Natural Hazards summarizes the global multi-temporal analysis of exposure to six major natural hazards: earthquakes, volcanoes, tsunamis, floods, tropical cyclone winds, and sea level surge. The exposure focuses on human settlements assessed through two variables: the global built-up and the global resident population. The two datasets are generated within the Global Human Settlement Project of the Joint Research Centre. They represent the core dataset of the Atlas of the Human Planet 2016 which provides empirical evidence on urbanization trends and dynamics. The figures presented in the Atlas 2017 show that exposure to natural hazards doubled in the last 40 years, both for built-up area and population. Earthquake is the hazard that accounts for the highest number of people potentially exposed. Flood, the most frequent natural disaster, potentially affects more people in Asia (76.9% of the global population exposed) and Africa (12.2%) than in other regions. Tropical cyclone winds threaten 89 countries in the world and the population exposed to cyclones increased from 1 billion in 1975 up to 1.6 billion in 2015. The country most at risk to tsunamis is Japan, whose population is 4 times more exposed than China, the second country on the ranking. Sea level surge affects the countries across the tropical region and China has one of the largest increase of population over the last four decades (plus 200 million people from 1990 to 2015). The figures presented in the Atlas are aggregate estimates at country level. The value of the GHSL layers used to generate the figures in this Atlas is that the data are available at fine scale and exposure and the rate of change in exposure can be computed for any area of the world. Researchers and policy makers are now allowed to aggregate exposure information at all geographical scale of analysis from the country level to the region, continent and global.JRC.E.1-Disaster Risk Managemen

A statistical analysis of the relationship between landscape heterogeneity and the quantization of remote sensing data

This report addresses the investigation of the relationship between the landscape heterogeneity and the sequencing of remote sensing imagery for the purpose of better understanding the parameters of the Symbolic Machine Learning developed within the Global Human Settlement Layer project. To address this issue statistical regression analysis was conducted between the sequences derived from the Landsat satellite data and different landscape metrics derived from land cover maps. The results show that only the Relative Patch Richness influences the number of sequences for different levels of image reduction levels. The Shannon Landscape Diversity Index seems to be related to the Number of Sequences in the image until a certain Level of Quantization that may be an indicator of the optimal parameter for the sequencing of the input satellite data. These results represent a good step forward in the attempt to automatize the parameters set of the Symbolic Machine Learning classifier.JRC.E.1-Disaster Risk Managemen

The European Settlement Map 2019 release

The ESM_2015 is the latest release of the European Settlement Map produced in the frame of the GHSL project. It is produced with the Global Human Settlement Layer (GHSL) technology of the Joint Research Centre (JRC) in collaboration with the Directorate General of Regional and Urban Policy. The workflow was executed on the JRC Big Data Analytics platform. It follows-up on the previous ESM_2012 derived from 2.5 m resolution SPOT-5/6 images acquired in the context of the pan-European GMES/Copernicus (Core_003) dataset for the reference year 2012. The ESM_2015 product exploits the Copernicus VHR_IMAGE_2015 dataset made of satellite images Pleiades, Deimos-02, WorldView-2, WorldView-3, GeoEye-01 and Spot 6/7 ranging from 2014 to 2016. Unlike the previous ESM versions, the built-up extraction is realized through supervised learning (and not only by means of image filtering and processing techniques) based on textural and morphological features. The workflow is fully automated and it does not include any post-processing. For the first time a new layer containing non-residential buildings was derived by using only remote sensing imagery and training data. The produced built-up map is delivered at 2 m pixel resolution (level 1 layer) while the residential/non-residential layer (level 2) is delivered at 10 m spatial resolution. ESM_2015 offers new opportunities in Earth observation related research by allowing to study urbanisation and related features across Europe in urban and rural areas, from continental to country perspective, from regional to local, until single blocks. ESM_2015 was validated against the LUCAS 2015 survey database both at 2 and 10 meters resolution (including also the non-residential class). The validation has resulted in a Balanced Accuracy of 0.81 for the 2 m resolution built-up layer and of 0.71 for the 10 m non-residential built-up layer.JRC.E.1-Disaster Risk Managemen

LUE User Guide: A tool to calculate the Land Use Efficiency and the SDG 11.3 indicator with the Global Human Settlement Layer

LUE tool stands for Land Use Efficiency tool and it is a tool developed by the Global Human Settlement Layer (GHSL) Team. This tool, developed in Python language, allows user calculating easily and quickly some indicators on the change of land in an area of interest. The tool is designed to be use with GHS Layers on Built-up area and population, but it can be easily adapted also to other input data. This guide provides instructions about installing and using the LUE tool in the open source software QGIS and provides suggestions for the output interpretation.JRC.E.1-Disaster Risk Managemen