Global Human Settlement Layer Research Articles

Mosaicking Copernicus Sentinel-1 Data at Global Scale

This paper presents a processing chain for handling big volume of remotely sensing data for generating 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. While these two components are necessary constituents of the presented mosaicking workflow, 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 are carried out during the presentation of the results.

Application of semi-automated settlement detection for an integrated topographic map information system update in South Africa

Complete, accurate and up-to-date topographic data is of considerable importance as it is widely required by different government agencies, non-governmental organisations, the private sector as well as the general public for urban mapping, rural development and environmental management, to mention but a few applications. Efficient automatic / semi-automatic methods for detecting settlements as change area indicators are required in order to achieve a sustainable up-to-date topographic database. This leads to the aim of this study which seeks to investigate the automated / semi-automated settlement detection from remote sensing imagery to enhance the topographic database update in the KwaZulu-Natal Province, South Africa. This study focuses on two methods for automated / semi-automated settlement detection using 2012 SPOT 5 imagery. The first method focused on geographic object based image analysis (GEOBIA) technique through the eCognition software application, while the second method, is based on the South African global human settlement layer (SA_GHSL). The GEOBIA provided 78% accuracy for settlement detection while SA_GHSL obtained 62.6% accuracy. The results from the automated / semi-automated methods provide the topographic update analyst to be drawn to more areas of new settlement development at an enhanced efficient rate. However a review on the enhancement of processing parameters applied on the automated / semi-automated methods is a research gap to be investigated to improve the accuracy of these methods.

Assessing the accuracy of multi-temporal built-up land layers across rural-urban trajectories in the United States

Global data on settlements, built-up land and population distributions are becoming increasingly available and represent important inputs to a better understanding of key demographic processes such as urbanization and interactions between human and natural systems over time. One persistent drawback that prevents user communities from effectively and objectively using these data products more broadly, is the absence of thorough and transparent validation studies. This study develops a validation framework for accuracy assessment of multi-temporal built-up land layers using integrated public parcel and building records as validation data. The framework is based on measures derived from confusion matrices and incorporates a sensitivity analysis for potential spatial offsets between validation and test data as well as tests for the effects of varying criteria of the abstract term built-up land on accuracy measures. Furthermore, the framework allows for accuracy assessments by strata of built-up density, which provides important insights on the relationship between classification accuracy and development intensity to better instruct and educate user communities on quality aspects that might be relevant to different purposes. We use data from the newly-released Global Human Settlement Layer (GHSL), for four epochs since 1975 and at fine spatial resolution (38m), in the United States for a demonstration of the framework. The results show very encouraging accuracy measures that vary across study areas, generally improve over time but show very distinct patterns across the rural-urban trajectories. Areas of higher development intensity are very accurately classified and highly reliable. Rural areas show low degrees of accuracy, which could be affected by misalignment between the reference data and the data under test in areas where built-up land is scattered and rare. However, a regression analysis, which examines how well GHSL can estimate built-up land using spatially aggregated analytical units, indicates that classification error is mainly of thematic nature. Thus, caution should be taken in using the data product in rural regions. The results can be useful in further improving classification procedures to create measures of the built environment. The validation framework can be extended to data-poor regions of the world using map data and Volunteered Geographic Information.

Visualising world population density as an interactive multi-scale map using the global human settlement population layer

ABSTRACTRecent advances in global population data are enabling new cartographic and analytical opportunities. The Global Human Settlement Layer is the first sub-1 km cell resolution global population density product released as open data, with many applications in the fields of global population dynamics, urban studies and natural hazard risk. There are several cartographic challenges with visualising this dataset due to the range of spatial scales that are of interest, and the extensive variation in the density of settlements patterns that exist across different regions of the globe. These challenges are tackled here using interactive mapping, allowing navigation from global to city-region scales. A detailed classification and colour scheme is developed to distinguish a wide range of densities at multiple scales. Additionally, interactive statistics are presented for direct numerical comparisons at both country and city scales, further enabling global density comparisons. The interactive map produced has received 30,000 users in four months, indicating the widespread interest in understanding global population density.

TOWARDS CONSISTENT MAPPING OF URBAN STRUCTURES – GLOBAL HUMAN SETTLEMENT LAYER AND LOCAL CLIMATE ZONES

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.

TOWARDS CONSISTENT MAPPING OF URBAN STRUCTURES – GLOBAL HUMAN SETTLEMENT LAYER AND LOCAL CLIMATE ZONES

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.

Assessment of the Added-Value of Sentinel-2 for Detecting Built-up Areas

Monitoring of the human-induced changes and the availability of reliable and methodologically consistent urban area maps are essential to support sustainable urban development on a global scale. The Global Human Settlement Layer (GHSL) is a project funded by the European Commission, Joint Research Centre, which aims at providing scientific methods and systems for reliable and automatic mapping of built-up areas from remote sensing data. In the frame of the GHSL, the opportunities offered by the recent availability of Sentinel-2 data are being explored using a novel image classification method, called Symbolic Machine Learning (SML), for detailed urban land cover mapping. In this paper, a preliminary test was implemented with the purpose of: (i) assessing the applicability of the SML classifier on Sentinel-2 imagery; (ii) evaluating the complementarity of Sentinel-1 and Sentinel-2; and (iii) understanding the added-value of Sentinel-2 with respect to Landsat for improving global high-resolution human settlement mapping. The overall objective is to explore areas of improvement, including the possibility of synergistic use of the different sensors. The results showed that noticeable improvement of the quality of the classification could be gained from the increased spatial detail and from the thematic contents of Sentinel-2 compared to the Landsat derived product as well as from the complementarity between Sentinel-1 and Sentinel-2 images.

How good is the map? A multi-scale cross-comparison framework for global settlement layers: Evidence from Central Europe

Abstract 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.

Assessing Spatiotemporal Agreement between Multi-Temporal Built-up Land Layers and Integrated Cadastral and Building Data

GIScience 2016 Short Paper Proceedings Assessing Spatiotemporal Agreement between Multi- Temporal Built-up Land Layers and Integrated Cadastral and Building Data Johannes H. Uhl 1 , Stefan Leyk 1 , Aneta J. Florczyk 2 , Martino Pesaresi 2 , Deborah Balk 3 University of Colorado Boulder, Department of Geography, Boulder, CO 80309, U.S.A. Email: {johannes.uhl; stefan.leyk}@colorado.edu European Commission – Joint Research Centre (JRC), Institute for the Protection and Security of the Citizen (IPSC), Global Security and Crisis Management Unit, 21027 Ispra, Italy Email: {martino.pesaresi; aneta.florczyk}@jrc.ec.europa.eu City University of New York, Institute for Demographic Research and Baruch College, New York, NY 10010, U.S.A. Email: deborah.balk@baruch.cuny.edu Abstract There is an increasing availability of multi-temporal land use and built-up land datasets. However, little research has been done regarding the spatiotemporal uncertainty of these data products. In this work we present an approach that has the potential to be applicable for spatiotemporal evaluation of the novel Global Human Settlement Layer (GHSL) created by automatic classification of global collections of Landsat data recorded in the epochs 1975, 1990, 2000, and 2014. The proposed approach produces the reference data by integrating publicly available parcel and building data and derives agreement statistics with the GHSL. 1. Introduction The Global Human Settlement Layer (GHSL) project aims to assess the human presence in the planet by estimating the amount of built-up area based on remote sensing data and census data (Pesaresi et al. 2015). In Pesaresi et al. (2013) the GHSL information production workflow was tested for sensors ranging between 0.5 and 10m spatial resolution, which usually perform very well in detection of built-up areas but are typically constrained regarding data access, redistribution rights, and are typically not available consistently for long periods of time. For these reasons, these data are difficult to use for spatiotemporally uniform and systematic extraction of built-up areas on global, national or even regional level. Therefore, the GHSL workflow was tested with global collections of publicly available Landsat imagery collected in the past 40 years (Pesaresi et al. 2016). The Landsat GHSL dataset is available as seamless global mosaic at high spatial resolution (approx. 38m) and for various points in time (1975, 1990, 2000, 2014, see Figure 1a). GHSL data offers promising opportunities for population projections, disaster management and risk assessment (Freire et al. 2015; Freire et al. 2016), as well as for analysing and modelling urban dynamics and land use change. Before such novel data products can be made available to the research community, an extensive quality assessment is required. However, such assessments are difficult due to the lack of reliable reference data particularly for earlier time periods and in less developed regions. In this paper we present and discuss a novel approach to evaluate multi-temporal spatial data on built-up land such as GHSL or developed land cover classes using publicly available parcel (cadastral) data integrated with building footprints in the U.S. The aim of this study is to establish a protocol for future testing the accuracy of fine-scale multi-temporal products derived from automatic classification of satellite data featuring the presence of built-up areas. The selection of the test areas discussed here are driven and constrained by the availability of the reference data and consequently the results derived are

Global Human Settlement Analysis for Disaster Risk Reduction

Abstract. The Global Human Settlement Layer (GHSL) is supported by the European Commission, Joint Research Center (JRC) in the frame of his institutional research activities. Scope of GHSL is developing, testing and applying the technologies and analysis methods integrated in the JRC Global Human Settlement analysis platform for applications in support to global disaster risk reduction initiatives (DRR) and regional analysis in the frame of the European Cohesion policy. GHSL analysis platform uses geo-spatial data, primarily remotely sensed and population. GHSL also cooperates with the Group on Earth Observation on SB-04-Global Urban Observation and Information, and various international partners andWorld Bank and United Nations agencies. Some preliminary results integrating global human settlement information extracted from Landsat data records of the last 40 years and population data are presented.

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

A general framework for processing high and very-high resolution imagery in support of a Global Human Settlement Layer (GHSL) is presented together with a discussion on the results of the first operational test of the production workflow. The test involved the mapping of 24.3 million km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of the Earth surface spread in four continents, corresponding to an estimated population of 1.3 billion people in 2010. The resolution of the input image data ranges from 0.5 to 10 meters, collected by a heterogeneous set of platforms including satellite SPOT (2 and 5), CBERS 2B, RapidEye (2 and 4), WorldView (1 and 2), GeoEye 1, QuickBird 2, Ikonos 2, and airborne sensors. Several imaging modes were tested including panchromatic, multispectral and pan-sharpened images. A new fully automatic image information extraction, generalization and mosaic workflow is presented that is based on multiscale textural and morphological image features extraction. New image feature compression and optimization are introduced, together with new learning and classification techniques allowing for the processing of HR/VHR image data using low-resolution thematic layers as reference. A new systematic approach for quality control and validation allowing global spatial and thematic consistency checking is proposed and applied. The quality of the results are discussed by sensor, band, resolution, and eco-regions. Critical points, lessons learned and next steps are highlighted.

Multiscale quality assessment of Global Human Settlement Layer scenes against reference data using statistical learning

A method for quality assessment of the Global Human Settlement Layer scenes against reference data is presented. It relies on two settlement metrics; the local average and gradient functions that quantify the notions of settlement density and flexible settlement limits respectively. They are both utilized as generalization functions for increasing the level of abstraction of the sets under comparison. Generalization compensates for inaccuracies of the automatic target extraction method and can be computed at multiple scales. The comparison between the target built-up layers and the reference data employs an ordered multi-scale, linear regression computing the goodness of fit measure R2. An optimized assessment procedure is investigated in a pilot study and is further employed in a big data exercise. A newly introduced quality metric returns the agreement between automatically extracted built-up from a set of 13605 scenes and the MODIS 500 urban layer, that was found too be as high as 91% for selected sensors. A final experiment attempts a performance increase at lower scales by correlating the target layer with automatically selected training subsets. At 50m the adjusted R2 increases by 3% with a mean squared error improvement of 2% compared to the performance achieved without statistical learning. The experiment suggests that the GHSL assessment at a global scale can be carried out based on limited high resolution reference data of minimal spatial coverage.