A Global Human Settlement Layer From Optical HR/VHR RS Data: Concept and First Results

Pesaresi, Martino; Guo, Huadong; Blaes, Xavier; Ehrlich, Daniele; Stefano, Ferri; Gueguen, Lionel; Halkia, Matina; Kauffmann, Mayeul; Kemper, Thomas; Lu, Linlin; Marín-Herrera, Mario Alberto; Ouzounis, Georgios K.; Scavazzon, Marco; Soille, Pierre; Syrris, Vasileios; Zanchetta, Luigi

doi:10.1109/jstars.2013.2271445

Cited by 368 publications

(296 citation statements)

References 55 publications

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“…Earlier, lower-resolution examples include the Global Land Cover (GLC2000) [38], GlobCover [39], IGBP-DISCover [40] or MODIS land-cover map [41] products. More recently, the high-resolution Global Human Settlement Layer (GHSL) [42] and Global Urban Footprint (GUF) [37] datasets have become available for urban area observation.…”

Section: Large Urban Areas Ii: Earth Observationmentioning

confidence: 99%

Are We in Boswash Yet? A Multi-Source Geodata Approach to Spatially Delimit Urban Corridors

Georg

Blaschke

Taubenböck

2018

IJGI

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Abstract:The delimitation of urban space is conceptually elusive and fuzzy. Commonly, urban areas are delimited through administrative boundaries. These artificial, fixed boundaries, however, do not necessarily represent the actual built-up extent, the urban catchment, or the economic linkage within and across neighboring metropolitan regions. For an approach to spatially delimit an urban corridor-a generically defined concept of a massive urban area-we use the Boston to Washington (Boswash) region as an example. This area has been consistently conceptualized in literature as bounded urban space. We develop a method to spatially delimit the urban corridor using multi-source geodata (built-up extent, infrastructure and socioeconomic data) which are based on a grid rather than on administrative units. Threshold approaches for the input data serve to construct Boswash as varying connected territorial spaces, allowing us to investigate the variability of possible spatial forms of the area, i.e., to overcome the simple dichotomous classification in favor of a probability-based differentiation. Our transparent multi-layer approach, validated through income data, can easily be modified by using different input datasets while maintaining the underlying idea that the likelihood of an area being part of an urban corridor is flexible, i.e., in our case a factor of how many input layers return positive results.

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Section: Large Urban Areas Ii: Earth Observationmentioning

confidence: 99%

Are We in Boswash Yet? A Multi-Source Geodata Approach to Spatially Delimit Urban Corridors

Georg

Blaschke

Taubenböck

2018

IJGI

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“…Output is consistently provided at 30 arc-sec spatial resolution giving indication on the distribution of manmade surfaces including buildings, roads and related elements [7]. Due to its relevance for a broad set of applications, global impervious surface or general built-up area mapping has been in the focus of attention for a while with data from different satellite sensors used and various approaches implemented (overview provided by [24]), recent efforts including high resolution products such as the Global Urban Footprint (GUF) [25] and the Global Human Settlement Layer (GHSL) [26]. The DMSP-derived ISA data set is unique in a sense that it does not directly extract built-up from satellite imagery but uses artificial night lighting as proxy measure.…”

Section: Impervious Surface Area (Isa) Data Derived From Dmsp-olsmentioning

confidence: 99%

Evaluating Multi-Sensor Nighttime Earth Observation Data for Identification of Mixed vs. Residential Use in Urban Areas

Aubrecht

Torres

2016

Remote Sensing

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This paper introduces a novel top-down approach to geospatially identify and distinguish areas of mixed use from predominantly residential areas within urban agglomerations. Under the framework of the World Bank's Central American Country Disaster Risk Profiles (CDRP) initiative, a disaggregated property stock exposure model has been developed as one of the key elements for disaster risk and loss estimation. Global spatial datasets are therefore used consistently to ensure wide-scale applicability and transferability. Residential and mixed use areas need to be identified in order to spatially link accordingly compiled property stock information. In the presented study, multi-sensor nighttime Earth Observation data and derivative products are evaluated as proxies to identify areas of peak human activity. Intense artificial night lighting in that context is associated with a high likelihood of commercial and/or industrial presence. Areas of low light intensity, in turn, can be considered more likely residential. Iterative intensity thresholding is tested for Cuenca City, Ecuador, in order to best match a given reference situation based on cadastral land use data. The results and findings are considered highly relevant for the CDRP initiative, but more generally underline the relevance of remote sensing data for top-down modeling approaches at a wide spatial scale.

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“…It is a fully automatic procedure in which the image information extraction workflow processes multi-resolution (0.5 m-75 m), multi-platform (e.g., SPOT, Landsat, Sentinel), multi-sensor (pan, multispectral), and multi-temporal image data successfully (Pesaresi et al, 2013). For example, the European Settlement Map 2014 (http://land.copernicus.eu/paneuropean/GHSL/) is based on SPOT5-6 satellite imagery.…”

Section: Global Human Settlement Layermentioning

confidence: 99%

Towards Consistent Mapping of Urban Structures – Global Human Settlement Layer and Local Climate Zones

Bechtel

Pesaresi

See

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Although more than half of the Earth's population live in urban areas, we know remarkably little about most cities and what we do know is incomplete (lack of coverage) and inconsistent (varying definitions and scale). While there have been considerable advances in the derivation of a global urban mask using satellite information, the complexity of urban structures, the heterogeneity of materials, and the multiplicity of spectral properties have impeded the derivation of universal urban structural types (UST). Further, the variety of UST typologies severely limits the comparability of such studies and although a common and generic description of urban structures is an essential requirement for the universal mapping of urban structures, such a standard scheme is still lacking. More recently, there have been two developments in urban mapping that have the potential for providing a standard approach: the Local Climate Zone (LCZ) scheme (used by the World Urban Database and Access Portal Tools project) and the Global Human Settlement Layer (GHSL) methodology by JRC. In this paper the LCZ scheme and the GHSL LABEL product were compared for selected cities. The comparison between both datasets revealed a good agreement at city and coarse scale, while the contingency at pixel scale was limited due to the mismatch in grid resolution and typology. At a 1 km scale, built-up as well as open and compact classes showed very good agreement in terms of correlation coefficient and mean absolute distance, spatial pattern, and radial distribution as a function of distance from town, which indicates that a decomposition relevant for modelling applications could be derived from both. On the other hand, specific problems were found for both datasets, which are discussed along with their general advantages and disadvantages as a standard for UST classification in urban remote sensing.

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A Global Human Settlement Layer From Optical HR/VHR RS Data: Concept and First Results

Cited by 368 publications

References 55 publications

Are We in Boswash Yet? A Multi-Source Geodata Approach to Spatially Delimit Urban Corridors

Are We in Boswash Yet? A Multi-Source Geodata Approach to Spatially Delimit Urban Corridors

Evaluating Multi-Sensor Nighttime Earth Observation Data for Identification of Mixed vs. Residential Use in Urban Areas

Towards Consistent Mapping of Urban Structures – Global Human Settlement Layer and Local Climate Zones

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