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

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

The future of road transport

A perfect storm of new technologies and new business models is transforming not only our vehicles, but everything about how we get around, and how we live our lives. The JRC report “The future of road transport - Implications of automated, connected, low-carbon and shared mobility” looks at some main enablers of the transformation of road transport, such as data governance, infrastructures, communication technologies and cybersecurity, and legislation. It discusses the potential impacts on the economy, employment and skills, energy use and emissions, the sustainability of raw materials, democracy, privacy and social fairness, as well as on the urban context. It shows how the massive changes on the horizon represent an opportunity to move towards a transport system that is more efficient, safer, less polluting and more accessible to larger parts of society than the current one centred on car ownership. However, new transport technologies, on their own, won't spontaneously make our lives better without upgrading our transport systems and policies to the 21st century. The improvement of governance and the development of innovative mobility solutions will be crucial to ensure that the future of transport is cleaner and more equitable than its car-centred present.JRC.C.4-Sustainable Transpor

Spatio-temporal pattern extraction from remote sensing image series : application on optical and radar data

Les Séries Temporelles d'Images Satellitaires (STIS), visant la même scène en évolution, sont très intéressantes parce qu'elles acquièrent conjointement des informations temporelles et spatiales. L'extraction de ces informations pour aider les experts dans l'interprétation des données satellitaires devient une nécessité impérieuse. Dans ce mémoire, nous exposons comment on peut adapter l'extraction de motifs séquentiels fréquents à ce contexte spatio-temporel dans le but d'identifier des ensembles de pixels connexes qui partagent la même évolution temporelle. La démarche originale est basée sur la conjonction de la contrainte de support avec différentes contraintes de connexité qui peuvent filtrer ou élaguer l'espace de recherche pour obtenir efficacement des motifs séquentiels fréquents groupés (MSFG) avec signification pour l'utilisateur. La méthode d'extraction proposée est non supervisée et basée sur le niveau pixel. Pour vérifier la généricité du concept de MSFG et la capacité de la méthode proposée d'offrir des résultats intéressants à partir des SITS, sont réalisées des expérimentations sur des données réelles optiques et radar.The Satellite Image Time Series (SITS), aiming the same scene in evolution, are of high interest as they capture both spatial and temporal information. The extraction of this infor- mation to help the experts interpreting the satellite data becomes a stringent necessity. In this work, we expound how to adapt frequent sequential patterns extraction to this spatiotemporal context in order to identify sets of connected pixels sharing a same temporal evolution. The original approach is based on the conjunction of support constraint with different constraints based on pixel connectivity that can filter or prune the search space in order to efficiently ob- tain Grouped Frequent Sequential (GFS) patterns that are meaningful to the end user. The proposed extraction method is unsupervised and pixel level based. To verify the generality of GFS-pattern concept and the proposed method capability to offer interesting results from SITS, real data experiments on optical and radar data are presented

Extraction de motifs spatio-temporels dans des séries d'images de télédétection : application à des données optiques et radar

The Satellite Image Time Series (SITS), aiming the same scene in evolution, are of high interest as they capture both spatial and temporal information. The extraction of this infor- mation to help the experts interpreting the satellite data becomes a stringent necessity. In this work, we expound how to adapt frequent sequential patterns extraction to this spatiotemporal context in order to identify sets of connected pixels sharing a same temporal evolution. The original approach is based on the conjunction of support constraint with different constraints based on pixel connectivity that can filter or prune the search space in order to efficiently ob- tain Grouped Frequent Sequential (GFS) patterns that are meaningful to the end user. The proposed extraction method is unsupervised and pixel level based. To verify the generality of GFS-pattern concept and the proposed method capability to offer interesting results from SITS, real data experiments on optical and radar data are presented.Les Séries Temporelles d'Images Satellitaires (STIS), visant la même scène en évolution, sont très intéressantes parce qu'elles acquièrent conjointement des informations temporelles et spatiales. L'extraction de ces informations pour aider les experts dans l'interprétation des données satellitaires devient une nécessité impérieuse. Dans ce mémoire, nous exposons comment on peut adapter l'extraction de motifs séquentiels fréquents à ce contexte spatio-temporel dans le but d'identifier des ensembles de pixels connexes qui partagent la même évolution temporelle. La démarche originale est basée sur la conjonction de la contrainte de support avec différentes contraintes de connexité qui peuvent filtrer ou élaguer l'espace de recherche pour obtenir efficacement des motifs séquentiels fréquents groupés (MSFG) avec signification pour l'utilisateur. La méthode d'extraction proposée est non supervisée et basée sur le niveau pixel. Pour vérifier la généricité du concept de MSFG et la capacité de la méthode proposée d'offrir des résultats intéressants à partir des SITS, sont réalisées des expérimentations sur des données réelles optiques et radar

The role of infrastructure for electric passenger cars uptake in Europe

Plug-in electric vehicles (PEV) can be a main lever towards a decarbonised road transport system. The PEV market uptake needs to be nurtured by appropriate support measures for users, for technological advances related to the vehicle and its components, and for all relevant recharging infrastructure deployment. This paper focuses on the role of PEV recharging infrastructure for electric passenger car uptake in Europe. It examines the status of road transport electrification, relevant policies, incentives and national plans. We find that the status and plans of PEV and recharging infrastructure and the corresponding support measures vary significantly between countries. The PEV share in the various analysed countries ranged in 2017 from 0.01% to 5.49% and is estimated to reach values between 0.05% and 12.71% in 2020. The corresponding ratio of PEV per one publicly accessible recharging point ranged between 1 and 60 and is estimated to vary between 3 and 161 in 2020. Diverging plans could lead to market fragmentation in the European Union (EU) and impede the EU-wide circulation of PEVs. The appropriate level of recharging infrastructure should be determined to both support PEV deployment and to prevent sunk investments. Different country experiences vis-à-vis PEV and infrastructure support could be useful to identify best practices.JRC.C.4-Sustainable Transpor

Benchmarking of the Symbolic Machine Learning classifier with state of the art image classification methods - application to remote sensing imagery

A new method for satellite data classification is presented. The method is based on symbolic machine learning (SML) techniques and is designed for working in complex and information-abundant environments, where it is important to assess relationships between different data layers in model-free and computational-effective modalities. In particular, the method is tailored for operating in earth observation data scenarios connoted by the following characteristics: i) they are made by a large number of data granules (scenes), ii) they are made by heterogeneous sensors and iii) they are mapping a large variety of different geographical areas in different data collection conditions. The volume, variety and partially unstructured nature of these scenarios can be associated with the characteristics of Big Data. The results of an experiment observing the behavior of the SML classifier by injecting increasing levels of noise in the training set are discussed. Spatial generalization, random thematic noise and spatial displacement noise are tested. Seven supervised classification algorithms have been considered for comparison: Maximum Likelihood, Logistic Regression, Linear Discriminant Analysis, Naive Bayes, Decision Tree, Random Forest and Support Vector Machine. According to the results of the experiment, the SML classifier performed very well providing outputs with comparable or better quality than the other classifiers. Furthermore, the better performances were released with a much less expensive computational cost. Consequently, the SML classifier was evaluated as the best available solution in the specific data scenario under consideration. Few applicative examples of the new SML classifier using Spot5, Sentinel1, and Sentinel2 data inputs are provided.JRC.G.2-Global security and crisis managemen

Statistical characterisation of the real transaction data gathered from electric vehicle charging stations

Despite the many environmental benefits that a massive diffusion of electric vehicles (EVs) could bring to the urban mobility and to society as a whole, numerous are the challenges that this could pose to the electricity distribution grid, particularly to its operation and development. While uncoordinated management of EVs can lead to load imbalances, current or voltage variation excess and steep power requests, properly designed and well-coordinated integration approaches can in contrast provide flexibility, hence value, to the whole electrical system. Such step can be achieved only if real data are available and real drivers’ behaviours are identified. This paper is based on a real dataset of 400,000 EV charging transactions. It shows and analyses an important set of key figures (charge time, idle time, connected time, power, and energy) depending on driver's behaviour in the Netherlands. From these figures, it emerges a key role of the uncertainty of the relevant variables due to the drivers’ behaviour. This requires a statistical characterisation of these variables, which generally leads to multi-modal probability distributions. Thereby, this paper develops a Beta Mixture Model to represent these multi-modal probability distributions. Based on the emerged statistical facts, a number of results and suggestions are provided, in order to contribute to the important debate on the role of EVs to move to a fully decarbonised society.JRC.C.3-Energy Security, Distribution and Market

Electric light commercial vehicles: are they the sleeping giant of electromobility?

Transport emissions need to be drastically decreased in order to put Europe on a path towards a long-term climate neutrality. Commercial transport, and especially last mile delivery is expected to grow because of the rise of e-commerce. In this frame, electric light commercial vehicles (eLCVs) can be a promising low-emission solution. Literature holistically analysing the potential of eLCVs as well as related support policies is sparse. This paper attempts to close this research gap. To this aim, the total cost of ownership (TCO) comparisons for eLCVs and benchmark vehicles are performed and support measures that target the improvement of the eLCV TCO are analysed. Various eLCV deployment scenarios until 2030 are explored and their impact on carbon dioxide (CO2) and other pollutant emissions as well as pollutant concentrations are calculated. It is found that while in several European Union (EU) countries eLCVs are already cost competitive, because of fiscal support, some remaining market barriers need to be overcome to pave the way to mass market deployment of eLCVs. High penetration of eLCVs alone can lead to a reduction of total transport CO2 emissions by more than 3% by 2030. For pollutant emissions, such as nitrogen oxide (NOx) and particulate matter (PM), the reduction would be equal or even higher. In the case of PM, this can translate to reductions in concentrations by nearly 2% in several urban areas by 2030. Carefully designed support policies could help to ensure that the potential of eLCVs as a low-emission alternative is fully leveraged in the EU.JRC.C.4-Sustainable Transpor