Big earth data analytics on Sentinel-1 and Landsat imagery in support to global human settlements mapping

Corbane, Christina; Pesaresi, Martino; Politis, Panagiotis; Syrris, Vasileios; Florczyk, Aneta J.; Soille, Pierre; Luca, Maffenini; Burger, Armin; Vasilev, V. E.; Rodríguez, Dario; Sabo, Filip; Dijkstra, Lewis; Kemper, Thomas

doi:10.1080/20964471.2017.1397899

Cited by 109 publications

(95 citation statements)

References 34 publications

(35 reference statements)

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“…Those data are produced in an equal-area projection at 250 m (GHS-POP 250 m) and 1 km (GHS-POP 1 km) spatial resolution. Residential population estimates for 1975, 1990, 2000, and 2015 were disaggregated from census or administrative units to grid cells based on the distribution of built-up areas in the Global Human Settlement Layer (GHSL) [51,52]. The disaggregation methodology is described in a conference scientific paper [40].…”

Section: The Ghs-pop Datamentioning

confidence: 99%

GHS-POP Accuracy Assessment: Poland and Portugal Case Study

Całka¹,

Bielecka²

2020

Remote Sensing

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The Global Human Settlement Population Grid (GHS-POP) the latest released global gridded population dataset based on remotely sensed data and developed by the EU Joint Research Centre, depicts the distribution and density of the total population as the number of people per grid cell. This study aims to assess the GHS-POP data accuracy based on root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) and the correlation coefficient. The study was conducted for Poland and Portugal, countries characterized by different population distribution as well as two spatial resolutions of 250 m and 1 km on the GHS-POP. The main findings show that as the size of administrative zones decreases (from NUTS (Nomenclature of Territorial Units for Statistics) to LAU (local administrative unit)) and the size of the GHS-POP increases, the difference between the population counts reported by the European Statistical Office and estimated by the GHS-POP algorithm becomes larger. At the national level, MAPE ranges from 1.8% to 4.5% for the 250 m and 1 km resolutions of GHS-POP data in Portugal and 1.5% to 1.6%, respectively in Poland. At the local level, however, the error rates range from 4.5% to 5.8% in Poland, for 250 m and 1 km, and 5.7% to 11.6% in Portugal, respectively. Moreover, the results show that for densely populated regions the GHS-POP underestimates the population number, while for thinly populated regions it overestimates. The conclusions of this study are expected to serve as a quality reference for potential users and producers of population density datasets.

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Section: The Ghs-pop Datamentioning

confidence: 99%

GHS-POP Accuracy Assessment: Poland and Portugal Case Study

Całka¹,

Bielecka²

2020

Remote Sensing

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“…While specific figures of such modifications are heterogeneous across products [16,43,44], it was collectively understood that the land conversion induced by the spatial expansion of urban areas is a prominent development challenge [45,46].Progressively refined thematic mapping products [47], [38], [41] paved the way for a new generation of layers that have reached the spatial detail to map small settlements (Figure 1), with products such as the built-up grid (GHS-BUILT) produced in the Global Human Settlement Layer (GHSL) framework at the European Commission-Joint Research Centre (EC-JRC), and the Global Urban Footprint (GUF) produced at the German Aerospace Agency (DLR). Both layers represent a new generation of EO products that possess high spatial resolution (decametric resolution), abstract artificial land as built-up areas and urban footprints [43,48,49], and have global coverage. Information extracted from these layers provides the most recent representation of the extent of human settlements -2012 in GUF and 2014 [43,50] and substantially contributes to the understanding of the extent of the urbanisation process [12,51].…”

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confidence: 99%

Multi-Scale Estimation of Land Use Efficiency (SDG 11.3.1) across 25 Years Using Global Open and Free Data

et al. 2019

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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.

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“…In Europe and Asia it detected more than Landsat, but not in other continents. It was shown by Corbane, Pesaresi et al () that S1 contributed to fewer omission errors in Africa and Asia compared to the Landsat product. MODIS detected the least built‐up pixels for almost all continents.…”

Section: Resultsmentioning

confidence: 96%

Comparison of built‐up area maps produced within the global human settlement framework

Sabo

Corbane

Florczyk

et al. 2018

Transactions in GIS

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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.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.Knowledge of the spatial distribution of human settlements and monitoring of urban expansion are crucial for a large number of applications, such as exposure mapping and risk assessment, infrastructure planning, biodiversity conservation, climate change, and urban development (Chrysoulakis et al., 2014;Triantakonstantis et al., 2015;Florczyk et al., 2016). One key for understanding worldwide urbanization processes and developing actions toward sustainable urban and rural development is the availability of detailed, up-to-date, accurate, and consistent-in time and space-information on human settlements. Human settlements are closely related to population distribution, as well as to social and economic development which, from the spatial perspective, is not directly measurable. Built-up areas, which refer to the physical space used for human habitation, represent the physical dimension of human settlements and the most appropriate surrogate for urbanization processes. The spatial aspect involved in the definition of built-up areas is what makes remote sensing appealing for extracting global and up-to-date information on the extent of the built-up environment that can be related directly to human settlements.

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Big earth data analytics on Sentinel-1 and Landsat imagery in support to global human settlements mapping

Cited by 109 publications

References 34 publications

GHS-POP Accuracy Assessment: Poland and Portugal Case Study

GHS-POP Accuracy Assessment: Poland and Portugal Case Study

Multi-Scale Estimation of Land Use Efficiency (SDG 11.3.1) across 25 Years Using Global Open and Free Data

Comparison of built‐up area maps produced within the global human settlement framework

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