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Built-up areas extraction and characterization from remote sensing images is essential for monitoring urbanization and the associated challenges. This work presents a novel integrated classification framework building on the Symbolic Machine Learning classifier and fully polarimetric PALSAR-2 to derive both the extent and vertical components of built-up areas from the same scene. It also explores the complementarity between ascending and descending orbits of PALSAR-2 for built-up areas detection. The experimental results in Chicago and Tokyo cities with different landscape and characteristics of built-up areas demonstrate that the proposed generic method can achieve three main challenges of urban remote sensing: i) enabling automated delineation of built-up areas at a spatial resolution of 5 meters with a balanced accuracy of 85% using globally available low resolution training data, ii) assessing the density of building height class with a RMSE of 0.25, 0.034, and 0.032 for the low-rise, mid-rise, and high-rise building density class, respectively and iii) dealing with the scattering components of buildings with different orientation angles by combining data from ascending and descending orbits for enhanced mapping of built-up areas.
Christina Corbane; Soushi Kato; Koki Iwao; Filip Sabo; Panagiotis Politis; Martino Pesaresi; Thomas Kemper. Leveraging ALOS-2 PALSAR-2 for Mapping Built-Up Areas and Assessing Their Vertical Component. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 13, 6473 -6483.
AMA StyleChristina Corbane, Soushi Kato, Koki Iwao, Filip Sabo, Panagiotis Politis, Martino Pesaresi, Thomas Kemper. Leveraging ALOS-2 PALSAR-2 for Mapping Built-Up Areas and Assessing Their Vertical Component. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 13 (99):6473-6483.
Chicago/Turabian StyleChristina Corbane; Soushi Kato; Koki Iwao; Filip Sabo; Panagiotis Politis; Martino Pesaresi; Thomas Kemper. 2020. "Leveraging ALOS-2 PALSAR-2 for Mapping Built-Up Areas and Assessing Their Vertical Component." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13, no. 99: 6473-6483.
Spatially consistent and up-to-date maps of human settlements are crucial for addressing policies related to urbanization and sustainability, especially in the era of an increasingly urbanized world. The availability of open and free Sentinel-2 data of the Copernicus Earth Observation program offers a new opportunity for wall-to-wall mapping of human settlements at a global scale. This paper presents a deep-learning-based framework for a fully automated extraction of built-up areas at a spatial resolution of 10 m from a global composite of Sentinel-2 imagery. A multi-neuro modeling methodology building on a simple Convolution Neural Networks architecture for pixel-wise image classification of built-up areas is developed. The core features of the proposed model are the image patch of size 5 × 5 pixels adequate for describing built-up areas from Sentinel-2 imagery and the lightweight topology with a total number of 1,448,578 trainable parameters and 4 2D convolutional layers and 2 flattened layers. The deployment of the model on the global Sentinel-2 image composite provides the most detailed and complete map reporting about built-up areas for reference year 2018. The validation of the results with an independent reference dataset of building footprints covering 277 sites across the world establishes the reliability of the built-up layer produced by the proposed framework and the model robustness. The results of this study contribute to cutting-edge research in the field of automated built-up areas mapping from remote sensing data and establish a new reference layer for the analysis of the spatial distribution of human settlements across the rural–urban continuum.
Christina Corbane; Vasileios Syrris; Filip Sabo; Panagiotis Politis; Michele Melchiorri; Martino Pesaresi; Pierre Soille; Thomas Kemper. Convolutional neural networks for global human settlements mapping from Sentinel-2 satellite imagery. Neural Computing and Applications 2020, 33, 6697 -6720.
AMA StyleChristina Corbane, Vasileios Syrris, Filip Sabo, Panagiotis Politis, Michele Melchiorri, Martino Pesaresi, Pierre Soille, Thomas Kemper. Convolutional neural networks for global human settlements mapping from Sentinel-2 satellite imagery. Neural Computing and Applications. 2020; 33 (12):6697-6720.
Chicago/Turabian StyleChristina Corbane; Vasileios Syrris; Filip Sabo; Panagiotis Politis; Michele Melchiorri; Martino Pesaresi; Pierre Soille; Thomas Kemper. 2020. "Convolutional neural networks for global human settlements mapping from Sentinel-2 satellite imagery." Neural Computing and Applications 33, no. 12: 6697-6720.
Large-scale land cover classification from satellite imagery is still a challenge due to the big volume of data to be processed, to persistent cloud-cover in cloud-prone areas as well as seasonal artefacts that affect spatial homogeneity. Sentinel-2 times series from Copernicus Earth Observation program offer a great potential for fine scale land cover mapping thanks to high spatial and temporal resolutions, with a decametric resolution and five-day repeat time. However, the selection of best available scenes, their download together with the requirements in terms of storage and computing resources pose restrictions for large-scale land cover mapping. The dataset presented in this paper corresponds to global cloud-free pixel based composite created from the Sentinel-2 data archive (Level L1C) available in Google Earth Engine for the period January 2017- December 2018. The methodology used for generating the image composite is described and the metadata associated with the 10 m resolution dataset is presented. The data with a total volume of 15 TB is stored on the Big Data platform of the Joint Research Centre. It can be downloaded per UTM grid zone, loaded into GIS clients and displayed easily thanks to pre-computed overviews.
C. Corbane; P. Politis; P. Kempeneers; D. Simonetti; P. Soille; A. Burger; M. Pesaresi; F. Sabo; V. Syrris; T. Kemper. A global cloud free pixel- based image composite from Sentinel-2 data. Data in Brief 2020, 31, 105737 .
AMA StyleC. Corbane, P. Politis, P. Kempeneers, D. Simonetti, P. Soille, A. Burger, M. Pesaresi, F. Sabo, V. Syrris, T. Kemper. A global cloud free pixel- based image composite from Sentinel-2 data. Data in Brief. 2020; 31 ():105737.
Chicago/Turabian StyleC. Corbane; P. Politis; P. Kempeneers; D. Simonetti; P. Soille; A. Burger; M. Pesaresi; F. Sabo; V. Syrris; T. Kemper. 2020. "A global cloud free pixel- based image composite from Sentinel-2 data." Data in Brief 31, no. : 105737.
The digital transformation of our society coupled with the increasing exploitation of natural resources makes sustainability challenges more complex and dynamic than ever before. These changes will unlikely stop or even decelerate in the near future. There is an urgent need for a new scientific approach and an advanced form of evidence-based decision-making towards the benefit of society, the economy, and the environment. To understand the impacts and interrelationships between humans as a society and natural Earth system processes, we propose a new engineering discipline, Big Earth Data science. This science is called to provide the methodologies and tools to generate knowledge from diverse, numerous, and complex data sources necessary to ensure a sustainable human society essential for the preservation of planet Earth. Big Earth Data science aims at utilizing data from Earth observation and social sensing and develop theories for understanding the mechanisms of how such a social-physical system operates and evolves. The manuscript introduces the universe of discourse characterizing this new science, its foundational paradigms and methodologies, and a possible technological framework to be implemented by applying an ecosystem approach. CASEarth and GEOSS are presented as examples of international implementation attempts. Conclusions discuss important challenges and collaboration opportunities.
Huadong Guo; Stefano Nativi; Dong Liang; Max Craglia; Lizhe Wang; Sven Schade; Christina Corban; Guojin He; Martino Pesaresi; Jianhui Li; Zeeshan Shirazi; Jie Liu; Alessandro Annoni. Big Earth Data science: an information framework for a sustainable planet. International Journal of Digital Earth 2020, 13, 743 -767.
AMA StyleHuadong Guo, Stefano Nativi, Dong Liang, Max Craglia, Lizhe Wang, Sven Schade, Christina Corban, Guojin He, Martino Pesaresi, Jianhui Li, Zeeshan Shirazi, Jie Liu, Alessandro Annoni. Big Earth Data science: an information framework for a sustainable planet. International Journal of Digital Earth. 2020; 13 (7):743-767.
Chicago/Turabian StyleHuadong Guo; Stefano Nativi; Dong Liang; Max Craglia; Lizhe Wang; Sven Schade; Christina Corban; Guojin He; Martino Pesaresi; Jianhui Li; Zeeshan Shirazi; Jie Liu; Alessandro Annoni. 2020. "Big Earth Data science: an information framework for a sustainable planet." International Journal of Digital Earth 13, no. 7: 743-767.
This letter introduces the new European Settlement Map (ESM) production workflow, presents some indicatory results, and discusses the validation process. Unlike the previous ESM versions, the built-up (BU) extraction is realized through supervised learning (not only by means of image filtering and processing techniques) based on textural and morphological features. Input data are the Copernicus very-high-resolution image collection for the reference year 2015 coming from a variety of sensors. The workflow is fully automated, and it does not include any postprocessing. For the first time, a new layer containing nonresidential buildings was derived by using only remote sensing imagery and training data. The produced BU map is delivered at 2-m-pixel resolution (level-1 layer), while the residential/nonresidential layer (level 2) is delivered at 10-m spatial resolution. More than 46,000 scenes were processed, and around 6 million km² of the European territory was mapped. The workflow was executed on the JRC Big Data platform. The validation showed a balanced accuracy of 0.81 and 0.91 for level 1 and 2 layers, respectively, and 0.71 for only the nonresidential layer.
C. Corbane; F. Sabo; V. Syrris; T. Kemper; P. Politis; M. Pesaresi; P. Soille; K. Ose. Application of the Symbolic Machine Learning to Copernicus VHR Imagery: The European Settlement Map. IEEE Geoscience and Remote Sensing Letters 2019, 17, 1153 -1157.
AMA StyleC. Corbane, F. Sabo, V. Syrris, T. Kemper, P. Politis, M. Pesaresi, P. Soille, K. Ose. Application of the Symbolic Machine Learning to Copernicus VHR Imagery: The European Settlement Map. IEEE Geoscience and Remote Sensing Letters. 2019; 17 (7):1153-1157.
Chicago/Turabian StyleC. Corbane; F. Sabo; V. Syrris; T. Kemper; P. Politis; M. Pesaresi; P. Soille; K. Ose. 2019. "Application of the Symbolic Machine Learning to Copernicus VHR Imagery: The European Settlement Map." IEEE Geoscience and Remote Sensing Letters 17, no. 7: 1153-1157.
Sustainable Development Goal (SDG) 11 aspires to “Make cities and human settlements inclusive, safe, resilient and sustainable”, and the introduction of an explicit urban goal testifies to the importance of urbanisation. The understanding of the process of urbanisation and the capacity to monitor the SDGs require a wealth of open, reliable, locally yet globally comparable data, and a fully-fledged data revolution. In this framework, the European Commission–Joint Research Centre has developed a suite of (open and free) data and tools named Global Human Settlement Layer (GHSL) which maps the human presence on Earth (built-up areas, population distribution and settlement typologies) between 1975 and 2015. The GHSL supplies information on the progressive expansion of built-up areas on Earth and population dynamics in human settlements, with both sources of information serving as baseline data to quantify land use efficiency (LUE), listed as a Tier II indicator for SDG 11 (11.3.1). In this paper, we present the profile of the LUE across several territorial scales between 1990 and 2015, highlighting diverse development trajectories and the land take efficiency of different human settlements. Our results show that (i) the GHSL framework allows us to estimate LUE for the entire planet at several territorial scales, opening the opportunity of lifting the LUE indicator from its Tier II classification; (ii) the current formulation of the LUE is substantially subject to path dependency; and (iii) it requires additional spatially-explicit metrics for its interpretation. We propose the Achieved Population Density in Expansion Areas and the Marginal Land Consumption per New Inhabitant metrics for this purpose. The study is planetary and multi-temporal in coverage, demonstrating the value of well-designed, open and free, fine-scale geospatial information on human settlements in supporting policy and monitoring progress made towards meeting the SDGs.
Marcello Schiavina; Michele Melchiorri; Christina Corbane; Aneta Florczyk; Sergio Freire; Martino Pesaresi; Thomas Kemper. Multi-Scale Estimation of Land Use Efficiency (SDG 11.3.1) across 25 Years Using Global Open and Free Data. Sustainability 2019, 11, 5674 .
AMA StyleMarcello Schiavina, Michele Melchiorri, Christina Corbane, Aneta Florczyk, Sergio Freire, Martino Pesaresi, Thomas Kemper. Multi-Scale Estimation of Land Use Efficiency (SDG 11.3.1) across 25 Years Using Global Open and Free Data. Sustainability. 2019; 11 (20):5674.
Chicago/Turabian StyleMarcello Schiavina; Michele Melchiorri; Christina Corbane; Aneta Florczyk; Sergio Freire; Martino Pesaresi; Thomas Kemper. 2019. "Multi-Scale Estimation of Land Use Efficiency (SDG 11.3.1) across 25 Years Using Global Open and Free Data." Sustainability 11, no. 20: 5674.
Linlin Lu; Huadong Guo; Christina Corbane; Qingting Li. Urban sprawl in provincial capital cities in China: evidence from multi-temporal urban land products using Landsat data. Science Bulletin 2019, 64, 955 -957.
AMA StyleLinlin Lu, Huadong Guo, Christina Corbane, Qingting Li. Urban sprawl in provincial capital cities in China: evidence from multi-temporal urban land products using Landsat data. Science Bulletin. 2019; 64 (14):955-957.
Chicago/Turabian StyleLinlin Lu; Huadong Guo; Christina Corbane; Qingting Li. 2019. "Urban sprawl in provincial capital cities in China: evidence from multi-temporal urban land products using Landsat data." Science Bulletin 64, no. 14: 955-957.
The revised and enhanced version of the new European Settlement Map is presented together with the first results. An added-value to the previous versions is the improved automatic detection of buildings, automatic extraction of water, extraction of building typology and an information layer allowing to derive city indicators. The ESM workflow is fully automatic and runs on the Joint Research Centre Earth Observation Data and Processing Platform.
Filip Sabo; Christina Corbane; Panagiotis Politis; Martino Pesaresi; Thomas Kemper. Update and improvement of the European Settlement map. 2019 Joint Urban Remote Sensing Event (JURSE) 2019, 1 -4.
AMA StyleFilip Sabo, Christina Corbane, Panagiotis Politis, Martino Pesaresi, Thomas Kemper. Update and improvement of the European Settlement map. 2019 Joint Urban Remote Sensing Event (JURSE). 2019; ():1-4.
Chicago/Turabian StyleFilip Sabo; Christina Corbane; Panagiotis Politis; Martino Pesaresi; Thomas Kemper. 2019. "Update and improvement of the European Settlement map." 2019 Joint Urban Remote Sensing Event (JURSE) , no. : 1-4.
This paper presents the analysis of Earth Observation data records collected between 1975 and 2014 for assessing the extent and temporal evolution of the built-up surface in the frame of the Global Human Settlement Layer project. The scale of the information produced by the study enables the assessment of the whole continuum of human settlements from rural hamlets to megacities. The study applies enhanced processing methods as compared to the first production of the GHSL baseline data. The major improvements include the use of a more refined learning set on built-up areas derived from Sentinel-1 data which allowed testing the added-value of incremental learning in big data analytics. Herein, the new features of the GHSL built-up grids and the methods are described and compared with the previous ones using a reference set of building footprints for 277 areas of interest. The results show a gradual improvement in the accuracy measures with a gain of 3.6% in the balanced accuracy, between the first production of the GHSL baseline and the latest GHSL multitemporal built-up grids. A validation of the multitemporal component is also conducted at the global scale establishing the reliability of the built-up layer across time.
Christina Corbane; Martino Pesaresi; Thomas Kemper; Panagiotis Politis; Aneta J. Florczyk; Vasileios Syrris; Michele Melchiorri; Filip Sabo; Pierre Soille. Automated global delineation of human settlements from 40 years of Landsat satellite data archives. Big Earth Data 2019, 3, 140 -169.
AMA StyleChristina Corbane, Martino Pesaresi, Thomas Kemper, Panagiotis Politis, Aneta J. Florczyk, Vasileios Syrris, Michele Melchiorri, Filip Sabo, Pierre Soille. Automated global delineation of human settlements from 40 years of Landsat satellite data archives. Big Earth Data. 2019; 3 (2):140-169.
Chicago/Turabian StyleChristina Corbane; Martino Pesaresi; Thomas Kemper; Panagiotis Politis; Aneta J. Florczyk; Vasileios Syrris; Michele Melchiorri; Filip Sabo; Pierre Soille. 2019. "Automated global delineation of human settlements from 40 years of Landsat satellite data archives." Big Earth Data 3, no. 2: 140-169.
The Global Human Settlement Layer (GHSL) produces new global spatial information, evidence-based analytics describing the human presence on the planet that is based mainly on two quantitative factors: (i) the spatial distribution (density) of built-up structures and (ii) the spatial distribution (density) of resident people. Both of the factors are observed in the long-term temporal domain and per unit area, in order to support the analysis of the trends and indicators for monitoring the implementation of the 2030 Development Agenda and the related thematic agreements. The GHSL uses various input data, including global, multi-temporal archives of high-resolution satellite imagery, census data, and volunteered geographic information. In this paper, we present a global estimate for the Land Use Efficiency (LUE) indicator—SDG 11.3.1, for circa 10,000 urban centers, calculating the ratio of land consumption rate to population growth rate between 1990 and 2015. In addition, we analyze the characteristics of the GHSL information to demonstrate how the original frameworks of data (gridded GHSL data) and tools (GHSL tools suite), developed from Earth Observation and integrated with census information, could support Sustainable Development Goals monitoring. In particular, we demonstrate the potential of gridded, open and free, local yet globally consistent, multi-temporal data in filling the data gap for Sustainable Development Goal 11. The results of our research demonstrate that there is potential to raise SDG 11.3.1 from a Tier II classification (manifesting unavailability of data) to a Tier I, as GHSL provides a global baseline for the essential variables called by the SDG 11.3.1 metadata.
Michele Melchiorri; Martino Pesaresi; Aneta J. Florczyk; Christina Corbane; Thomas Kemper. Principles and Applications of the Global Human Settlement Layer as Baseline for the Land Use Efficiency Indicator—SDG 11.3.1. ISPRS International Journal of Geo-Information 2019, 8, 96 .
AMA StyleMichele Melchiorri, Martino Pesaresi, Aneta J. Florczyk, Christina Corbane, Thomas Kemper. Principles and Applications of the Global Human Settlement Layer as Baseline for the Land Use Efficiency Indicator—SDG 11.3.1. ISPRS International Journal of Geo-Information. 2019; 8 (2):96.
Chicago/Turabian StyleMichele Melchiorri; Martino Pesaresi; Aneta J. Florczyk; Christina Corbane; Thomas Kemper. 2019. "Principles and Applications of the Global Human Settlement Layer as Baseline for the Land Use Efficiency Indicator—SDG 11.3.1." ISPRS International Journal of Geo-Information 8, no. 2: 96.
Geo-information on settlements from Earth Observation offers a base for objective and scalable monitoring of the evolution of cities and settlements, including their location, extent and other attributes. In this work, we deploy the best available global knowledge on the presence of human settlements and built-up structures derived from Earth Observation to advance the understanding of the human presence on Earth. We start from a concept of Generalised Settlement Area to identify the Earth surface within which any built-up structure is present. We further characterise the resulted map by using an agreement map among the state of the art of remote sensing products mapping built-up areas or other strictly related semantic abstractions as urban areas or artificial surfaces. The agreement map is formed by a grid of 1 km2, where each cell is classified according to the number of EO-derived products reporting any positive occurrence of the abstractions related to the presence of built-up structures. The paper describes the characteristics of the Generalised Settlement Area, the differences in the agreement map across geographic regions of the world, and outlines the implications for potential users of the EO-derived products used in this study.
A. J. Florczyk; M. Melchiorri; J. Zeidler; C. Corbane; M. Schiavina; S. Freire; F. Sabo; P. Politis; T. Esch; M. Pesaresi. The Generalised Settlement Area: mapping the Earth surface in the vicinity of built-up areas. International Journal of Digital Earth 2019, 13, 45 -60.
AMA StyleA. J. Florczyk, M. Melchiorri, J. Zeidler, C. Corbane, M. Schiavina, S. Freire, F. Sabo, P. Politis, T. Esch, M. Pesaresi. The Generalised Settlement Area: mapping the Earth surface in the vicinity of built-up areas. International Journal of Digital Earth. 2019; 13 (1):45-60.
Chicago/Turabian StyleA. J. Florczyk; M. Melchiorri; J. Zeidler; C. Corbane; M. Schiavina; S. Freire; F. Sabo; P. Politis; T. Esch; M. Pesaresi. 2019. "The Generalised Settlement Area: mapping the Earth surface in the vicinity of built-up areas." International Journal of Digital Earth 13, no. 1: 45-60.
Data on global population distribution are a strategic resource currently in high demand in an age of new Development Agendas that call for universal inclusiveness of people. However, quality, detail, and age of census data varies significantly by country and suffers from shortcomings that propagate to derived population grids and their applications. In this work, the improved capabilities of recent remote sensing-derived global settlement data to detect and mitigate major discrepancies with census data is explored. Open layers mapping built-up presence were used to revise census units deemed as ‘unpopulated’ and to harmonize population distribution along coastlines. Automated procedures to detect and mitigate these anomalies, while minimizing changes to census geometry, preserving the regional distribution of population, and the overall counts were developed, tested, and applied. The two procedures employed for the detection of deficiencies in global census data obtained high rates of true positives, after verification and validation. Results also show that the targeted anomalies were significantly mitigated and are encouraging for further uses of free and open geospatial data derived from remote sensing in complementing and improving conventional sources of fundamental population statistics.
Sergio Freire; Marcello Schiavina; Aneta J. Florczyk; Kytt MacManus; Martino Pesaresi; Christina Corbane; Olena Borkovska; Jane Mills; Linda Pistolesi; John Squires; Richard Sliuzas. Enhanced data and methods for improving open and free global population grids: putting ‘leaving no one behind’ into practice. International Journal of Digital Earth 2018, 13, 61 -77.
AMA StyleSergio Freire, Marcello Schiavina, Aneta J. Florczyk, Kytt MacManus, Martino Pesaresi, Christina Corbane, Olena Borkovska, Jane Mills, Linda Pistolesi, John Squires, Richard Sliuzas. Enhanced data and methods for improving open and free global population grids: putting ‘leaving no one behind’ into practice. International Journal of Digital Earth. 2018; 13 (1):61-77.
Chicago/Turabian StyleSergio Freire; Marcello Schiavina; Aneta J. Florczyk; Kytt MacManus; Martino Pesaresi; Christina Corbane; Olena Borkovska; Jane Mills; Linda Pistolesi; John Squires; Richard Sliuzas. 2018. "Enhanced data and methods for improving open and free global population grids: putting ‘leaving no one behind’ into practice." International Journal of Digital Earth 13, no. 1: 61-77.
There is an increasing availability of geospatial data describing patterns of human settlement and population such as various global remote-sensing based built-up land layers, fine-grained census-based population estimates, and publicly available cadastral and building footprint data. This development constitutes new integrative modeling opportunities to characterize the continuum of urban, peri-urban, and rural settlements and populations. However, little research has been done regarding the agreement between such data products in measuring human presence which is measured by different proxy variables (i.e. presence of built-up structures derived from different remote sensors, census-derived population counts, or cadastral land parcels). In this work, we quantitatively evaluate and cross-compare the ability of such data to model the urban continuum, using a unique, integrated validation database of cadastral and building footprint data, U.S. census data, and three different versions of the Global Human Settlement Layer (GHSL) derived from remotely sensed data. We identify advantages and shortcomings of these data types across different geographic settings in the U.S., which will inform future data users on implications of data accuracy and suitability for a given application, even in data-poor regions of the world.
Johannes H. Uhl; Hamidreza Zoraghein; Stefan Leyk; Deborah Balk; Christina Corbane; Vasileios Syrris; Aneta J. Florczyk. Exposing the urban continuum: implications and cross-comparison from an interdisciplinary perspective. International Journal of Digital Earth 2018, 13, 22 -44.
AMA StyleJohannes H. Uhl, Hamidreza Zoraghein, Stefan Leyk, Deborah Balk, Christina Corbane, Vasileios Syrris, Aneta J. Florczyk. Exposing the urban continuum: implications and cross-comparison from an interdisciplinary perspective. International Journal of Digital Earth. 2018; 13 (1):22-44.
Chicago/Turabian StyleJohannes H. Uhl; Hamidreza Zoraghein; Stefan Leyk; Deborah Balk; Christina Corbane; Vasileios Syrris; Aneta J. Florczyk. 2018. "Exposing the urban continuum: implications and cross-comparison from an interdisciplinary perspective." International Journal of Digital Earth 13, no. 1: 22-44.
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 quality, reduced noise pollution, decreased ambient temperature and psychological restoration. Evidence also shows that the amount of vegetation, known as ‘greenness’, in densely populated areas, can also be an indicator of the relative wealth of a neighbourhood. The ‘grey-green divide’, the contrast between built-up areas with a dominant grey colour and green spaces, is taken as a proxy indicator of sustainable management of cities and planning of urban growth. Consistent and continuous assessment of greenness in cities is therefore essential for monitoring progress towards the United Nations Sustainable Development Goal 11. The availability of multi-temporal greenness information from Landsat data archives together with data derived from the city centres database of the Global Human Settlement Layer (GHSL) initiative, offers a unique perspective to quantify and analyse changes in greenness across 10,323 urban centres all around the globe. In this research, we assess differences between greenness within and outside the built-up area for all the urban centres described by the city centres database of the GHSL. We also analyse changes in the amount of green space over time considering changes in the built-up areas in the periods 1990, 2000 and 2014. The results show an overall trend of increased greenness between 1990 and 2014 in most cities. The effect of greening is observed also for most of the 32 world megacities. We conclude that using simple yet effective approaches exploiting open and free global data it is possible to provide quantitative information on the greenness of cities and its changes over time. This information is of direct interest for urban planners and decision-makers to mitigate urban related environmental and social impacts.
Christina Corbane; Pesaresi Martino; Politis Panagiotis; Florczyk J. Aneta; Melchiorri Michele; Freire Sergio; Schiavina Marcello; Ehrlich Daniele; Naumann Gustavo; Kemper Thomas. The grey-green divide: multi-temporal analysis of greenness across 10,000 urban centres derived from the Global Human Settlement Layer (GHSL). International Journal of Digital Earth 2018, 13, 101 -118.
AMA StyleChristina Corbane, Pesaresi Martino, Politis Panagiotis, Florczyk J. Aneta, Melchiorri Michele, Freire Sergio, Schiavina Marcello, Ehrlich Daniele, Naumann Gustavo, Kemper Thomas. The grey-green divide: multi-temporal analysis of greenness across 10,000 urban centres derived from the Global Human Settlement Layer (GHSL). International Journal of Digital Earth. 2018; 13 (1):101-118.
Chicago/Turabian StyleChristina Corbane; Pesaresi Martino; Politis Panagiotis; Florczyk J. Aneta; Melchiorri Michele; Freire Sergio; Schiavina Marcello; Ehrlich Daniele; Naumann Gustavo; Kemper Thomas. 2018. "The grey-green divide: multi-temporal analysis of greenness across 10,000 urban centres derived from the Global Human Settlement Layer (GHSL)." International Journal of Digital Earth 13, no. 1: 101-118.
The Global Human Settlement Layer (GHSL) produces new global spatial information, evidence-based analytics and knowledge describing the human presence on the planet based mainly on two quantitative factors: i) the spatial distribution (density) of built-up structures and ii) the spatial distribution (density) of resident people. Both factors are observed in the long-term temporal domain and per uniform surface units in order to support trends and indicators for monitoring the implementation of international framework agreements. The GHSL uses various input data including global, multi-temporal archives of fine-scale satellite imagery, census data, and volunteered geographic information. In this paper, we present the characteristics of GHSL information to demonstrate how original frameworks of data and tools rooted on Earth Observation could support Sustainable Development Goals monitoring. In particular, we demonstrate the reach of gridded, open and free, local yet globally consistent, multi-temporal data in filling the data gap for the Sustainable Development Goal 11. Our experiments produce a global estimate for the Land Use Efficiency indicator (SDG 11.3.1) for 10,000 urban centers, calculating the ratio of land consumption to population growth rate that took place between 1990 and 2015. The results of our research demonstrate that there is a potential to lift SDG 11.3.1 from a tier 2 as GHSL provides a global baseline for the essential variables called by the SDG 11.3.1 metadata.
Michele Melchiorri; Martino Pesaresi; Aneta J. Florczyk; Christina Corbane; Thomas Kemper. Principles and Applications of the Global Human Settlement Layer as Baseline for the Land Use Efficiency Indicator –SDG 11.3.1. 2018, 1 .
AMA StyleMichele Melchiorri, Martino Pesaresi, Aneta J. Florczyk, Christina Corbane, Thomas Kemper. Principles and Applications of the Global Human Settlement Layer as Baseline for the Land Use Efficiency Indicator –SDG 11.3.1. . 2018; ():1.
Chicago/Turabian StyleMichele Melchiorri; Martino Pesaresi; Aneta J. Florczyk; Christina Corbane; Thomas Kemper. 2018. "Principles and Applications of the Global Human Settlement Layer as Baseline for the Land Use Efficiency Indicator –SDG 11.3.1." , no. : 1.
The validation of built‐up areas derived from different sensors is crucial for gaining a deeper understanding of the consistency and interoperability between them. This article presents the methodology and results of an inter‐sensor comparison of built‐up area data derived from Landsat, Sentinel‐1, Sentinel‐2, and SPOT5/SPOT6. The assessment was performed for 13 cities across the world for which cartographic reference building footprints were available. Several validation approaches were used: cumulative built‐up curve analysis, pixel‐by‐pixel performance metrics, and regression analysis. The results indicate that Sentinel‐1 and Sentinel‐2 contribute greatly to improved built‐up area detection compared to Landsat, within the global human settlement framework. However, Sentinel‐2 tends to show high omission errors while Landsat tends to have the lowest omission error. The built‐up area obtained from SPOT5/SPOT6 shows high consistency with the reference data for all European cities, and hence can potentially be considered as a reference dataset for wall‐to‐wall validation in Europe.
Filip Sabo; Christina Corbane; Aneta J. Florczyk; Stefano Ferri; Martino Pesaresi; Thomas Kemper. Comparison of built‐up area maps produced within the global human settlement framework. Transactions in GIS 2018, 22, 1406 -1436.
AMA StyleFilip Sabo, Christina Corbane, Aneta J. Florczyk, Stefano Ferri, Martino Pesaresi, Thomas Kemper. Comparison of built‐up area maps produced within the global human settlement framework. Transactions in GIS. 2018; 22 (6):1406-1436.
Chicago/Turabian StyleFilip Sabo; Christina Corbane; Aneta J. Florczyk; Stefano Ferri; Martino Pesaresi; Thomas Kemper. 2018. "Comparison of built‐up area maps produced within the global human settlement framework." Transactions in GIS 22, no. 6: 1406-1436.
Exposure is reported to be the biggest determinant of disaster risk, it is continuously growing and by monitoring and understanding its variations over time it is possible to address disaster risk reduction, also at the global level. This work uses Earth observation image archives to derive information on human settlements that are used to quantify exposure to five natural hazards. This paper first summarizes the procedure used within the global human settlement layer (GHSL) project to extract global built-up area from 40 year deep Landsat image archive and the procedure to derive global population density by disaggregating population census data over built-up area. Then it combines the global built-up area and the global population density data with five global hazard maps to produce global layers of built-up area and population exposure to each single hazard for the epochs 1975, 1990, 2000, and 2015 to assess changes in exposure to each hazard over 40 years. Results show that more than 35% of the global population in 2015 was potentially exposed to earthquakes (with a return period of 475 years); one billion people are potentially exposed to floods (with a return period of 100 years). In light of the expansion of settlements over time and the changing nature of meteorological and climatological hazards, a repeated acquisition of human settlement information through remote sensing and other data sources is required to update exposure and risk maps, and to better understand disaster risk and define appropriate disaster risk reduction strategies as well as risk management practices. Regular updates and refined spatial information on human settlements are foreseen in the near future with the Copernicus Sentinel Earth observation constellation that will measure the evolving nature of exposure to hazards. These improvements will contribute to more detailed and data-driven understanding of disaster risk as advocated by the Sendai Framework for Disaster Risk Reduction.
Daniele Ehrlich; Michele Melchiorri; Aneta J. Florczyk; Martino Pesaresi; Thomas Kemper; Christina Corbane; Sergio Freire; Marcello Schiavina; Alice Siragusa. Remote Sensing Derived Built-Up Area and Population Density to Quantify Global Exposure to Five Natural Hazards over Time. Remote Sensing 2018, 10, 1378 .
AMA StyleDaniele Ehrlich, Michele Melchiorri, Aneta J. Florczyk, Martino Pesaresi, Thomas Kemper, Christina Corbane, Sergio Freire, Marcello Schiavina, Alice Siragusa. Remote Sensing Derived Built-Up Area and Population Density to Quantify Global Exposure to Five Natural Hazards over Time. Remote Sensing. 2018; 10 (9):1378.
Chicago/Turabian StyleDaniele Ehrlich; Michele Melchiorri; Aneta J. Florczyk; Martino Pesaresi; Thomas Kemper; Christina Corbane; Sergio Freire; Marcello Schiavina; Alice Siragusa. 2018. "Remote Sensing Derived Built-Up Area and Population Density to Quantify Global Exposure to Five Natural Hazards over Time." Remote Sensing 10, no. 9: 1378.
This paper presents a processing chain for handling big volume of remotely sensing data when the aim is the generation of wide extent mosaics. More specifically, the data under consideration are level-1 ground range detected Sentinel-1 products with dual polarisation (VV+VH or HH+HV). Two approaches for a) distribution discretization accompanied by false color composition and b) image rendering and mosaicking are proposed. These two components are necessary constituents of the presented mosaicking workflow, nevertheless they can operate independently of each other. The design of the processing chain satisfies three objectives: i) contrasting derivative products of the input Sentinel-1 imagery such as the Global Human Settlement Layer, ii) adapting on a high-throughput computing system for fast execution, and iii) allowing potential extensions to more complex applications such as the image classification. Fast processing, process automation, incremental adjustment and information distinction are the main advantages of the proposed method. Elaboration and focus on these features is carried out during the presentation of the results.
Vasileios Syrris; Christina Corbane; Martino Pesaresi; Pierre Soille. Mosaicking Copernicus Sentinel-1 Data at Global Scale. IEEE Transactions on Big Data 2018, 6, 547 -557.
AMA StyleVasileios Syrris, Christina Corbane, Martino Pesaresi, Pierre Soille. Mosaicking Copernicus Sentinel-1 Data at Global Scale. IEEE Transactions on Big Data. 2018; 6 (3):547-557.
Chicago/Turabian StyleVasileios Syrris; Christina Corbane; Martino Pesaresi; Pierre Soille. 2018. "Mosaicking Copernicus Sentinel-1 Data at Global Scale." IEEE Transactions on Big Data 6, no. 3: 547-557.
Continuous global-scale mapping of human settlements in the service of international agreements calls for massive volume of multi-source, multi-temporal, and multi-scale earth observation data. In this paper, the latest developments in terms of processing big earth observation data for the purpose of improving the Global Human Settlement Layer (GHSL) data are presented. Two experiments with Sentinel-1 and Landsat data collections were run leveraging on the Joint Research Centre Earth Observation Data and Processing Platform. A comparative analysis of the results of built-up areas extraction from different remote sensing data and processing workflows shows how the information production supported by data-intensive computing infrastructure for optimization and multiple testing can improve the output information reliability and consistency within the GHSL scope. The paper presents the processing workflows and the results of the two main experiments, giving insights into the enhanced mapping capabilities gained by analyzing Sentinel-1 and Landsat data-sets, and the lessons learnt in terms of handling and processing big earth observation data.
Christina Corbane; Martino Pesaresi; Panagiotis Politis; Vasileios Syrris; Aneta J. Florczyk; Pierre Soille; Luca Maffenini; Armin Burger; Veselin Vasilev; Dario Rodriguez; Filip Sabo; Lewis Dijkstra; Thomas Kemper. Big earth data analytics on Sentinel-1 and Landsat imagery in support to global human settlements mapping. Big Earth Data 2017, 1, 118 -144.
AMA StyleChristina Corbane, Martino Pesaresi, Panagiotis Politis, Vasileios Syrris, Aneta J. Florczyk, Pierre Soille, Luca Maffenini, Armin Burger, Veselin Vasilev, Dario Rodriguez, Filip Sabo, Lewis Dijkstra, Thomas Kemper. Big earth data analytics on Sentinel-1 and Landsat imagery in support to global human settlements mapping. Big Earth Data. 2017; 1 (1-2):118-144.
Chicago/Turabian StyleChristina Corbane; Martino Pesaresi; Panagiotis Politis; Vasileios Syrris; Aneta J. Florczyk; Pierre Soille; Luca Maffenini; Armin Burger; Veselin Vasilev; Dario Rodriguez; Filip Sabo; Lewis Dijkstra; Thomas Kemper. 2017. "Big earth data analytics on Sentinel-1 and Landsat imagery in support to global human settlements mapping." Big Earth Data 1, no. 1-2: 118-144.
This article presents an experiment in which multi-temporal interferometric coherence calculated from 6-days Sentinel-1A and Sentinel-1B image pairs and backscatter intensity σ° are jointly used for the extraction of built-up areas in the framework of the symbolic machine learning classification. The results obtained with the proposed approach confirm the enhanced capabilities of discriminating built-up areas when using coherence information in comparison to two available global human settlement layers derived: (1) from Landsat optical data and (2) from Sentinel-1 ground range detected data and based on backscatter intensity σ° only. The experiment carried out in The Netherlands Randstad area is expected to be indicative of the results obtainable for urban areas having similar structures and types of built-up.
C. Corbane; G. Lemoine; Martino Pesaresi; Thomas Kemper; F. Sabo; S. Ferri; V. Syrris. Enhanced automatic detection of human settlements using Sentinel-1 interferometric coherence. International Journal of Remote Sensing 2017, 39, 842 -853.
AMA StyleC. Corbane, G. Lemoine, Martino Pesaresi, Thomas Kemper, F. Sabo, S. Ferri, V. Syrris. Enhanced automatic detection of human settlements using Sentinel-1 interferometric coherence. International Journal of Remote Sensing. 2017; 39 (3):842-853.
Chicago/Turabian StyleC. Corbane; G. Lemoine; Martino Pesaresi; Thomas Kemper; F. Sabo; S. Ferri; V. Syrris. 2017. "Enhanced automatic detection of human settlements using Sentinel-1 interferometric coherence." International Journal of Remote Sensing 39, no. 3: 842-853.