Automated global delineation of human settlements from 40 years of Landsat satellite data archives

Corbane, Christina; Pesaresi, Martino; Kemper, Thomas; Politis, Panagiotis; Florczyk, Aneta J.; Syrris, Vasileios; Melchiorri, Michele; Sabo, Filip; Soille, Pierre

doi:10.1080/20964471.2019.1625528

Cited by 134 publications

(91 citation statements)

References 33 publications

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“…To evaluate the accuracy of our method, we compare our derived urban areas with one well-known global urban map -the Global Human Settlement Layer (GHSL) [14], which is produced by the European Commission (EC) using Landsat images. We use the GHSL as reference data since EC has released the 2015 version recently, which is closer to the date of our results (2016) than most of the other global urban maps (2010 and before).…”

Section: Comparison With Global Urban Mapsmentioning

confidence: 99%

Quantifying urban areas with multi-source data based on percolation theory

Cao

Dong

et al. 2020

Remote Sensing of Environment

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Quantifying urban areas is crucial for addressing associated urban issues such as environmental and sustainable problems. Remote sensing data, especially the nighttime light images, have been widely used to delineate urbanized areas across the world. Meanwhile, some emerging urban data, such as volunteered geographical information (e.g., OpenStreetMap) and social sensing data (e.g., mobile phone, social media), have also shown great potential in revealing urban boundaries and dynamics. However, consistent and robust methods to quantify urban areas from these multi-source data have remained elusive. Here, we propose a percolation-based method to extract urban areas from these multi-source urban data. We derive the optimal urban/non-urban threshold by considering the critical nature of urban systems with the support of the percolation theory. Furthermore, we apply the method with three open-source datasets -population, road, and nighttime light -to 28 countries. We show that the proposed method captures the similar urban characteristics in terms of urban areas from multi-source data, and Zipf's law holds well in most countries. The derived urban areas by different datasets show good agreement with the Global Human Settlement Layer (GHSL) and can be further improved by data fusion. Our study not only provides an efficient method to quantify urban areas with open-source data, but also deepens the understanding of urban systems and sheds some light on the multi-source data fusion in geographical fields.lacking in many developing countries. Second, the data collection process for MAs is time-consuming and expensive, making it unable to capture the rapid urbanization process in fast-growing regions. Third, the standard to define MAs is different in different countries. There is still a lack of a unified approach to obtain functional urban areas, which can be applicable to all countries.

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Section: Comparison With Global Urban Mapsmentioning

confidence: 99%

Quantifying urban areas with multi-source data based on percolation theory

Cao

Dong

et al. 2020

Remote Sensing of Environment

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“…For the assessment of LCR, the GHSL multi-temporal grids of a built-up surface were used (GHS-BUILT). Built-up areas were mapped from Landsat satellite data archives using a fully automated information extraction method [48]. The GHS-BUILT product encodes the presence of built-up surfaces in grid cells of 1 km and 250 m for the epochs 1975, 1990, 2000 and 2015. For the assessment of PGR, the GHSL population grids for the corresponding epochs were used (GHS-POP).…”

Section: Input Population and Built-up Datasetsmentioning

confidence: 99%

“…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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“…indicators 15.2 on forest monitoring and indicator 14.1 on water quality monitoring) (Anderson et al 2017). The GEO Human Planet Initiative supports UN-Habitat in the provision of global baseline data on built-up areas (Corbane et al 2019) and population dynamics (Freire et al 2020) for reporting on SDG 11, related to sustainable cities and communities. The baseline datasets were derived by combining different sources of remote sensing, census surveys, and other socio-economic variables.…”

Section: Geossmentioning

confidence: 99%

Big Earth Data science: an information framework for a sustainable planet

Guo

Nativi

Liang

et al. 2020

International Journal of Digital Earth

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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 decisionmaking 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. ARTICLE HISTORY

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Automated global delineation of human settlements from 40 years of Landsat satellite data archives

Cited by 134 publications

References 33 publications

Quantifying urban areas with multi-source data based on percolation theory

Quantifying urban areas with multi-source data based on percolation theory

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

Big Earth Data science: an information framework for a sustainable planet

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