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This review analyses peer-reviewed scientific publications and policy documents that use built-up density, population density and settlement typology spatial grids from the Global Human Settlement Layer (GHSL) project to quantify human presence and processes for sustainability. Such open and free grids provide detailed time series spanning 1975–2015 developed with consistent approaches. Improving our knowledge of cities and settlements by measuring their size extent, as well as the societal processes occurring within settlements, is key to understanding their impact on the local, regional and global environment for addressing global sustainability and the integrity of planet Earth. The reviewed papers are grouped around five main topics: Quantifying human presence; assessing settlement growth over time; estimating societal impact, assessing natural hazard risk and impact, and generating indicators for international framework agreements and policy documents. This review calls for continuing to refine and expand the work on societal variables that, when combined with essential variables including those for climate, biodiversity and ocean, can improve our understanding of the societal impact on the biosphere and help to monitor progress towards local, regional and planetary sustainability.
Daniele Ehrlich; Sergio Freire; Michele Melchiorri; Thomas Kemper. Open and Consistent Geospatial Data on Population Density, Built-Up and Settlements to Analyse Human Presence, Societal Impact and Sustainability: A Review of GHSL Applications. Sustainability 2021, 13, 7851 .
AMA StyleDaniele Ehrlich, Sergio Freire, Michele Melchiorri, Thomas Kemper. Open and Consistent Geospatial Data on Population Density, Built-Up and Settlements to Analyse Human Presence, Societal Impact and Sustainability: A Review of GHSL Applications. Sustainability. 2021; 13 (14):7851.
Chicago/Turabian StyleDaniele Ehrlich; Sergio Freire; Michele Melchiorri; Thomas Kemper. 2021. "Open and Consistent Geospatial Data on Population Density, Built-Up and Settlements to Analyse Human Presence, Societal Impact and Sustainability: A Review of GHSL Applications." Sustainability 13, no. 14: 7851.
The Degree of Urbanisation is a new definition of cities, towns and semi-dense areas, and rural areas endorsed by the UN Statistical Commission. The urban population share according to the Degree of Urbanisation is similar to the one based on national definitions in the Americas, Europe and Oceania, but considerably higher in Africa and Asia. An empirical analysis and a comparison of concepts suggest that towns are likely to be classified as rural areas in Africa and Asia and as urban areas in other parts of the world. The paper shows that cities cover only a small share of land, but this share doubled over the past forty years, as has the number of cities. Although cities have expanded rapidly, their population grew even faster leading to higher densities. The paper tests two classic urban facts: 1) the cities and towns as defined by the Degree of Urbanisation closely follow Zipf's law 2) the population shares in urban areas, cities and especially metropolitan areas are positively and significantly correlated with the level of economic development. Lastly, the sensitivity of the classification of population and land are tested by varying the population size and density thresholds as well using a different global population grid.
Lewis Dijkstra; Aneta J. Florczyk; Sergio Freire; Thomas Kemper; Michele Melchiorri; Martino Pesaresi; Marcello Schiavina. Applying the Degree of Urbanisation to the globe: A new harmonised definition reveals a different picture of global urbanisation. Journal of Urban Economics 2020, 103312 .
AMA StyleLewis Dijkstra, Aneta J. Florczyk, Sergio Freire, Thomas Kemper, Michele Melchiorri, Martino Pesaresi, Marcello Schiavina. Applying the Degree of Urbanisation to the globe: A new harmonised definition reveals a different picture of global urbanisation. Journal of Urban Economics. 2020; ():103312.
Chicago/Turabian StyleLewis Dijkstra; Aneta J. Florczyk; Sergio Freire; Thomas Kemper; Michele Melchiorri; Martino Pesaresi; Marcello Schiavina. 2020. "Applying the Degree of Urbanisation to the globe: A new harmonised definition reveals a different picture of global urbanisation." Journal of Urban Economics , no. : 103312.
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.
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.
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.
Scientists use Essential Climate Variables to understand and model the Earth’s climate. Complementary to the Climate Variables this paper introduces global built-up area and population density, referred to as Essential Societal Variables, that can be used to model human activities and the impact of climate induced hazards on society. Climate impact scenarios inform policy makers on current and future risk and on the cost for mitigation and adaptation measures. The global built-up area and global population densities are generated from Earth observation image archives and from national population census data in the framework of the Global Human Settlement Layer (GHSL) project. The layers are produced with fine granularity for four epochs: 1975, 1990, 2000 and 2015, and will be updated on a regular basis with open satellite imagery. The paper discusses the relevance of global built-up area and population density for a number of policy areas, in particular to understand regional and global urbanization processes and for use in operational crisis management and risk assessment. The paper also provides examples of global statistics on exposure to natural hazards based on the two ESVs and their use in policy making. Finally, the paper discusses the potential of using population and built-up area for developing indicators to monitor the progress in Agenda 2030 including the Sustainable Development Goals (SDGs).
D. Ehrlich; T. Kemper; M. Pesaresi; C. Corbane. Built-up area and population density: Two Essential Societal Variables to address climate hazard impact. Environmental Science & Policy 2018, 90, 73 -82.
AMA StyleD. Ehrlich, T. Kemper, M. Pesaresi, C. Corbane. Built-up area and population density: Two Essential Societal Variables to address climate hazard impact. Environmental Science & Policy. 2018; 90 ():73-82.
Chicago/Turabian StyleD. Ehrlich; T. Kemper; M. Pesaresi; C. Corbane. 2018. "Built-up area and population density: Two Essential Societal Variables to address climate hazard impact." Environmental Science & Policy 90, no. : 73-82.
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.
In the last few decades the magnitude and impacts of planetary urban transformations have become increasingly evident to scientists and policymakers. The ability to understand these processes remained limited in terms of territorial scope and comparative capacity for a long time: data availability and harmonization were among the main constraints. Contemporary technological assets, such as remote sensing and machine learning, allow for analyzing global changes in the settlement process with unprecedented detail. The Global Human Settlement Layer (GHSL) project set out to produce detailed datasets to analyze and monitor the spatial footprint of human settlements and their population, which are key indicators for the global policy commitments of the 2030 Development Agenda. In the GHSL, Earth Observation plays a key role in the detection of built-up areas from the Landsat imagery upon which population distribution is modelled. The combination of remote sensing imagery and population modelling allows for generating globally consistent and detailed information about the spatial distribution of built-up areas and population. The GHSL data facilitate a multi-temporal analysis of human settlements with global coverage. The results presented in this article focus on the patterns of development of built-up areas, population and settlements. The article reports about the present status of global urbanization (2015) and its evolution since 1990 by applying to the GHSL the Degree of Urbanisation definition of the European Commission Directorate General for Regional and Urban Policy (DG-Regio) and the Statistical Office of the European Communities (EUROSTAT). The analysis portrays urbanization dynamics at a regional level and per country income classes to show disparities and inequalities. This study analyzes how the 6.1 billion urban dwellers are distributed worldwide. Results show the degree of global urbanization (which reached 85% in 2015), the more than 100 countries in which urbanization has increased between 1990 and 2015, and the tens of countries in which urbanization is today above the global average and where urbanization grows the fastest. The paper sheds light on the key role of urban areas for development, on the patterns of urban development across the regions of the world and on the role of a new generation of data to advance urbanization theory and reporting.
Michele Melchiorri; Aneta J. Florczyk; Sergio Freire; Marcello Schiavina; Martino Pesaresi; Thomas Kemper. Unveiling 25 Years of Planetary Urbanization with Remote Sensing: Perspectives from the Global Human Settlement Layer. Remote Sensing 2018, 10, 768 .
AMA StyleMichele Melchiorri, Aneta J. Florczyk, Sergio Freire, Marcello Schiavina, Martino Pesaresi, Thomas Kemper. Unveiling 25 Years of Planetary Urbanization with Remote Sensing: Perspectives from the Global Human Settlement Layer. Remote Sensing. 2018; 10 (5):768.
Chicago/Turabian StyleMichele Melchiorri; Aneta J. Florczyk; Sergio Freire; Marcello Schiavina; Martino Pesaresi; Thomas Kemper. 2018. "Unveiling 25 Years of Planetary Urbanization with Remote Sensing: Perspectives from the Global Human Settlement Layer." Remote Sensing 10, no. 5: 768.
This chapter explains the characteristics and description of the global human settlement layer (GHSL) data. It illustrates the step-by-step method of calculating the land use efficiency (LUE) indicator. This method is based on a geospatial and temporal analysis of raster data. The calculation does not require any preliminary knowledge in remote sensing, however, basic knowledge in the handling and processing of geospatial data is required. The method for calculating the LUE indicator from GHSL data can be easily applied to any datasets on built-up areas and population. Among the developments envisaged within the framework of GHSL, there is the provision of information on the volume of built-up areas on a global scale. This will be used for a more precise modeling of population data and to improve the estimation of the LUE indicator.
Christina Corbane; Panagiotis Politis; Martino Pesaresi; Thomas Kemper; Alice Siragusa. Estimation of Land Use Efficiency from the Global Human Settlement Layer (GHSL). QGIS and Applications in Territorial Planning 2018, 39 -52.
AMA StyleChristina Corbane, Panagiotis Politis, Martino Pesaresi, Thomas Kemper, Alice Siragusa. Estimation of Land Use Efficiency from the Global Human Settlement Layer (GHSL). QGIS and Applications in Territorial Planning. 2018; ():39-52.
Chicago/Turabian StyleChristina Corbane; Panagiotis Politis; Martino Pesaresi; Thomas Kemper; Alice Siragusa. 2018. "Estimation of Land Use Efficiency from the Global Human Settlement Layer (GHSL)." QGIS and Applications in Territorial Planning , no. : 39-52.
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.
Daniele Ehrlich; Aneta J. Florczyk; Andreea Julea; Thomas Kemper; Martino Pesaresi; Vasileios Syrris. Measuring and monitoring the extent of human settlements. Urban Expansion, Land Cover and Soil Ecosystem Services 2017, 33 -58.
AMA StyleDaniele Ehrlich, Aneta J. Florczyk, Andreea Julea, Thomas Kemper, Martino Pesaresi, Vasileios Syrris. Measuring and monitoring the extent of human settlements. Urban Expansion, Land Cover and Soil Ecosystem Services. 2017; ():33-58.
Chicago/Turabian StyleDaniele Ehrlich; Aneta J. Florczyk; Andreea Julea; Thomas Kemper; Martino Pesaresi; Vasileios Syrris. 2017. "Measuring and monitoring the extent of human settlements." Urban Expansion, Land Cover and Soil Ecosystem Services , no. : 33-58.
This paper evaluates the quality of Global Human Settlement Layer (GHSL) data (10 m pixels) derived from a 2010 QuickBird image of the city of Kampala, Uganda. The evaluation is carried out through visual and statistical comparison with a reference data set consisting of building footprints of the same year. Seven sample areas from 35-194 Ha in size were used for the comparison. Results show that although the GHSL layer gives a strong visual impression of the major morphology of the city, there are considerable misclassifications at pixel level.
Richard Sliuzas; Monika Kuffer; Thomas Kemper. Assessing the quality of Global Human Settlement Layer products for Kampala, Uganda. 2017 Joint Urban Remote Sensing Event (JURSE) 2017, 1 -4.
AMA StyleRichard Sliuzas, Monika Kuffer, Thomas Kemper. Assessing the quality of Global Human Settlement Layer products for Kampala, Uganda. 2017 Joint Urban Remote Sensing Event (JURSE). 2017; ():1-4.
Chicago/Turabian StyleRichard Sliuzas; Monika Kuffer; Thomas Kemper. 2017. "Assessing the quality of Global Human Settlement Layer products for Kampala, Uganda." 2017 Joint Urban Remote Sensing Event (JURSE) , no. : 1-4.
Malgorzata Jenerowicz; Thomas Kemper. An improved automated procedure for informal and temporary dwellings detection and enumeration, using mathematical morphology operators on VHR satellite data. Remote Sensing Technologies and Applications in Urban Environments II 2016, 100080O -100080O-11.
AMA StyleMalgorzata Jenerowicz, Thomas Kemper. An improved automated procedure for informal and temporary dwellings detection and enumeration, using mathematical morphology operators on VHR satellite data. Remote Sensing Technologies and Applications in Urban Environments II. 2016; ():100080O-100080O-11.
Chicago/Turabian StyleMalgorzata Jenerowicz; Thomas Kemper. 2016. "An improved automated procedure for informal and temporary dwellings detection and enumeration, using mathematical morphology operators on VHR satellite data." Remote Sensing Technologies and Applications in Urban Environments II , no. : 100080O-100080O-11.
The exploitation of resources, if not properly managed, can lead to spoiling natural habitats as well as to threatening people’s health, livelihoods and security. The paper discusses a multi-scale Earth observation-based approach to provide independent information related to exploitation activities of natural resources for countries which are experiencing armed conflict. The analyses are based on medium to very high spatial resolution optical satellite data. Object-based image analysis is used for information extraction at these different scales. On a subnational level, conflict-related land cover changes as an indication of potential hot spots for exploitation activities are classified. The regional assessment provides information about potential activity areas of resource exploitation, whereas on a local scale, a site-specific assessment of exploitation areas is performed. The study demonstrates the potential of remote sensing for supporting the monitoring and documentation of natural resource exploitation in conflict regions.
Elisabeth Schoepfer; Kristin Spröhnle; Olaf Kranz; Xavier Blaes; Jan Kolomaznik; Filip Hilgert; Tomas Bartalos; Thomas Kemper. Towards a multi-scale approach for an Earth observation-based assessment of natural resource exploitation in conflict regions. Geocarto International 2016, 32, 1139 -1158.
AMA StyleElisabeth Schoepfer, Kristin Spröhnle, Olaf Kranz, Xavier Blaes, Jan Kolomaznik, Filip Hilgert, Tomas Bartalos, Thomas Kemper. Towards a multi-scale approach for an Earth observation-based assessment of natural resource exploitation in conflict regions. Geocarto International. 2016; 32 (10):1139-1158.
Chicago/Turabian StyleElisabeth Schoepfer; Kristin Spröhnle; Olaf Kranz; Xavier Blaes; Jan Kolomaznik; Filip Hilgert; Tomas Bartalos; Thomas Kemper. 2016. "Towards a multi-scale approach for an Earth observation-based assessment of natural resource exploitation in conflict regions." Geocarto International 32, no. 10: 1139-1158.
Mapping of settlement areas from space is entering a new era. With the recently developed Global Urban Footprint (based on radar data from TanDEM-X) and the Global Human Settlement Layer (based on optical data), two new initiatives that promise to map complex settlement patterns at global scales and unprecedented spatial resolutions are about to enter the scientific and map user community. However, comparative studies on these layers' strengths and weaknesses, especially in terms of their potential added value with regard to existing lower resolution maps, as well as their assessed accuracy are still absent. In this regard, we introduce a multi-scale cross-comparison framework that uses the best existing urban maps as a benchmark. To paint a complete picture, we simultaneously address several components of map accuracy including relative inter-map agreement, absolute accuracies and pattern-based classification differences. This framework is applied to present regionally representative results from two Central European test sites. In this, we find that the new base maps bring decisive advancements in preserving the small-scale complexity of global human settlement patterns beyond urban core areas. Relative inter-map comparison exposes low density settlement regions traditionally under-represented by lower resolution maps that are now recognized. Absolute metrics such as the Kappa coefficient of agreement (K ) show that accuracies of the new high resolution layers (K¯ = 0.56–0.58) nearly double those of existing products. Beyond, they feature substantial consistency between urban (K¯ = 0.46–0.50) and rural landscapes (K¯ = 0.41–0.45). Results from pattern-based exploration further reveal significant correlation of accuracies with physical pattern variations such as settlement density and mark a clear shift of accuracies from large to medium and small patch sizes. This differentiated view on classification accuracies shows that the new generation of urban maps constitutes a significantly enhanced spatial representation of large-scale settlement patterns.
M. Klotz; Thomas Kemper; C. Geiß; T. Esch; H. Taubenböck. How good is the map? A multi-scale cross-comparison framework for global settlement layers: Evidence from Central Europe. Remote Sensing of Environment 2016, 178, 191 -212.
AMA StyleM. Klotz, Thomas Kemper, C. Geiß, T. Esch, H. Taubenböck. How good is the map? A multi-scale cross-comparison framework for global settlement layers: Evidence from Central Europe. Remote Sensing of Environment. 2016; 178 ():191-212.
Chicago/Turabian StyleM. Klotz; Thomas Kemper; C. Geiß; T. Esch; H. Taubenböck. 2016. "How good is the map? A multi-scale cross-comparison framework for global settlement layers: Evidence from Central Europe." Remote Sensing of Environment 178, no. : 191-212.
An application of a general methodology for processing very high-resolution imagery to produce a European Settlement Map (ESM) in support of policy-makers is presented. The process mapped around 10 million km2 of the European continent. The input image data are satellite SPOT-5/6 pan-sharpened multispectral images of 2.5- and 1.5-m spatial resolution, respectively. This is the first time that remote sensing technology demonstrates capability to produce a continental information layer using 2.5-m input images. Moreover, it is the highest resolution continental map produced so far. The presented workflow is data-driven and consists in fully automatic image information extraction based on textural and morphological image analysis. The learning method allows the processing of high-resolution image data using coarse resolution thematic layers as reference. Validation shows an overall accuracy of 96% with omission and commission errors less than 4% and 1%, respectively.
Aneta Jadwiga Florczyk; Stefano Ferri; Vasileios Syrris; Thomas Kemper; Matina Halkia; Pierre Soille; Martino Pesaresi. A New European Settlement Map From Optical Remotely Sensed Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2015, 9, 1978 -1992.
AMA StyleAneta Jadwiga Florczyk, Stefano Ferri, Vasileios Syrris, Thomas Kemper, Matina Halkia, Pierre Soille, Martino Pesaresi. A New European Settlement Map From Optical Remotely Sensed Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2015; 9 (5):1978-1992.
Chicago/Turabian StyleAneta Jadwiga Florczyk; Stefano Ferri; Vasileios Syrris; Thomas Kemper; Matina Halkia; Pierre Soille; Martino Pesaresi. 2015. "A New European Settlement Map From Optical Remotely Sensed Data." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9, no. 5: 1978-1992.