Global Urban Footprint Research Articles

On the use of Global Urban Footprint to the Polish settlement vector database development

On the use of Global Urban Footprint to the Polish settlement vector database development

Quantitative assessment and comparison of urban patterns in Germany and the United States

In this paper, we present methods to assess the homogeneity of the settlement landscape and to identify settlement clusters in terms of location and size. Due to various input data, methodologies, and spatial concepts, there are only a few international comparative research studies with quantitative, spatial approaches to date. Existing studies are mostly qualitative and empirically hardly replicable. Here, we introduce two novel methodological approaches to describe urban land patterns in a numerically stable, consistent and globally comparable manner. The first method evaluates the multi-scale homogeneity of the settlements and the respective density distributions in a purely non-parametric approach. The second method transfers the settlement patterns into a Gaussian Mixture Model via hierarchical multi-scale clustering in order to describe each cluster by the parameters of a 2D Gaussian distribution. In order to proof the robustness of both approaches, two different reference units are considered for the interpretation of the results: OECD functional urban areas and standardized subsets of 200 km × 200 km around the same central cities. We show that German urban areas are characterized by a higher heterogeneity within a smaller neighbourhood with an almost symmetric density distribution in comparison to urban areas in the U.S. Furthermore, German urban areas possess an increasing homogeneity with increasing size, i.e., the larger the urban agglomeration, the more uniform it appears. It is the contrary in the U.S. These findings are substantiated by the analysis of individual city centres. In Germany, many clusters of similar size define the urban region, whereas in the U.S. one large dominating cluster exceeds most neighbouring settlements significantly. The study is performed on the Global Urban Footprint Density, which assigns a density value to each urban pixel of about 30 m by 30 m. In this way, the approach is a blueprint to be extended to urban patterns of any spatial unit worldwide.

Built-Up Area Extraction from GF-3 SAR Data Based on a Dual-Attention Transformer Model

Built-up area (BA) extraction using synthetic aperture radar (SAR) data has emerged as a potential method in urban research. Currently, typical deep-learning-based BA extractors show high false-alarm rates in the layover areas and subsurface bedrock, which ignore the surrounding information and cannot be directly applied to large-scale BA mapping. To solve the above problems, a novel transformer-based BA extraction framework for SAR images is proposed. Inspired by SegFormer, we designed a BA extractor with multi-level dual-attention transformer encoders. First, the hybrid dilated convolution (HDC) patch-embedding module keeps the surrounding information of the input patches. Second, the channel self-attention module is designed for dual-attention transformer encoders and global modeling. The multi-level structure is employed to produce the coarse-to-fine semantic feature map of BAs. About 1100 scenes of Gaofen-3 (GF-3) data and 200 scenes of Sentinel-1 data were used in the experiment. Compared to UNet, PSPNet, and SegFormer, our model achieved an 85.35% mean intersection over union (mIoU) and 94.75% mean average precision (mAP) on the test set. The proposed framework achieved the best results in both mountainous and plain terrains. The experiments using Sentinel-1 shows that the proposed method has a good generalization ability with different SAR data sources. Finally, the BA map of China for 2020 was obtained with an overall accuracy of about 86%, which shows high consistency with the global urban footprint. The above experiments proved the effectiveness and robustness of the proposed framework in large-scale BA mapping.

Assessing the suitability of urban-oriented land cover products for mapping rural settlements

In recent years, new settlement mapping products have become available at the global and continental scale. Although accuracy assessments have indicated the high quality of these products, assessments were performed mainly on urban areas. However, there is also a need to monitor rural settlement development, as it is often located in proximity to biodiversity hotspots. In this paper, we verified the suitability of three settlement products (i.e., Global Urban Footprint – GUF, European Settlement Map – ESM and Open Street Map – OSM) to detect rural settlements in the Carpathian ecoregion. Two independent accuracy assessments indicated that the GUF captures rural settlements most effectively (overall accuracy – OA – 65.4% and 92.5% depending on the procedure). In contrast, the ESM overestimated settlements (OA – 49.5% and 90.8%), while the OSM (OA – 61.2% and 90.2%) was the most inconsistent source of settlement data. A regional comparison indicated some deviations from these accuracies, reflecting the variability of settlement structures within the study area. This study highlights that although the GUF was the best-performing product in mapping rural settlements across the whole study area, the settlement information it provided was rather conservative, and rural settlements are still insufficiently represented in all tested datasets.

Multitemporal Change Detection Analysis in an Urbanized Environment Based upon Sentinel-1 Data

The German Ruhr area is a highly condensed urban area that experienced a tremendous structural change over recent decades with the replacement of the coal and steel industries by other sectors. Consequently, a lot of major land cover changes happened. To retrospectively quantify such land cover changes, this study analysed synthetic aperture radar images of the Sentinel-1 satellites by applying the Google Earth Engine. Three satellite images are analysed by the multitemporal difference-adjusted dispersion threshold approach to capture land cover changes such as demolished buildings and new buildings by applying a threshold. This approach uses synthetic aperture radar data that are rarely considered in previously existing land cover change services. Urbanization or urban sprawl leads to changes in the urban form globally. These can be caused, for example, by migration or regionally by structural change, etc., such as in the study area presented here. The results are validated with reference data sets, which are publicly available nationally (e.g., house contour lines, normalized digital terrain model, digital orthophotos) or which are publicly available globally like the Global Urban Footprint and the World Settlement Footprint. Based on this, land cover changes could be identified for 21 locations within the study area of the city of Bochum.

Spatial patterns of urbanising landscapes in the North Indian Punjab show features predicted by fractal theory

Understanding and governing human settlement patterns is a major challenge of the urban age. While rural settlements emerge as parts of agricultural landscapes, cities typically evolve in economically strategic locations, and over time form hierarchical systems of cities. Purposeful planning and the collective, self-organized behavior of the inhabitants interact in the development of regional settlement patterns. Since self-organizing systems often produce fractal patterns in nature, this study combines approaches of land use science, city ranking, and urban planning under a fractal theory framework, to analyze the settlement system of the Indian Punjab. Scaling levels were defined by discontinuities in the size distribution of built-up areas (Global Urban Footprint), which correlated to population-based classifications (r = 0.9591). Self-similarity across scales was supported by geo-statistical similarity (p < 0.05) of distances and angles between settlements of successive classes, and the overall fractal dimension of DB = 1.95. When compared to a modeled Sierpinski Carpet, more than 50% of the settlements met the fractal geometry rules at larger scales. The spatial distribution of small villages, however, deviated, indicating a scale-related shift in organizing principles. Explicitly acknowledging cross-scale relations and self-organisation in regional planning policies may lead to more sustainable settlement structures that are in harmony with natural system properties.

Are night-time lights a good proxy of economic activity in rural areas in middle and low-income countries? Examining the empirical evidence from Colombia

The use of satellite imagery, particularly night-time lights, has flourished in the last 20 years for socioeconomic studies. The intensity of the lights captured through remote sensing is frequently used as a proxy of different socioeconomic indicators. While some studies found a high correlation between night-time lights intensity and Gross Domestic Product, there has been an inconclusive debate about the validity of this assumption that night-time lights can serve as a good proxy for the economic development to sub-national level studies, particularly in rural areas of middle and lowincome countries. We test the suitability of night-time lights from publicly available data sources for estimating Regional Domestic Product (RDP) across municipalities with different degrees of urbanization in Colombia. We use a series of cross-sectional regression models to compare correlation between municipality RDP and luminosity from different sources for 2012 (DMSP, VIIRS, harmonized DMSP/VIIRS, and harmonized DMSP/VIIRS masked with Global Urban Footprint), as well as multilevel regression models to estimate RDP time-series from 2011 to 2018. Our findings reveal that all compared night-time light products can serve as a good indicator of municipal RDP patterns, while VIIRS data presents the best model fit. Harmonized data that have extensive temporal night-time light records from 2011 to 2018 were significantly correlated with RDP time-series. For seven population size levels – from big cities (>500,000 inhabitants) to rural areas (<5,000 inhabitants), the results present a comparatively higher model fit for urban than rural areas. The use of Global Urban Footprint further improved model fit for large cities but worsened rural RDP estimates. Therefore, our analysis underscores that the use of night-time lights can be a very valuable method to estimate patterns of socioeconomic change at the municipal level in medium and low-income countries and thus may help to implement better sustainability-oriented policies.

Deriving exclusion maps from C-band SAR time-series in support of floodwater mapping

Synthetic Aperture Radar (SAR) intensity is used as an input to many flood-mapping algorithms. The appearance of floodwater tends to cause a substantial decrease of backscatter intensity over scarcely vegetated terrain. However, limitations exist in areas where the SAR backscatter is not sufficiently sensitive to surface changes, e.g. shadow areas due to topography or obstacles on the ground, densely forested areas, sand, etc. Thus, we argue that it is of paramount importance to complement any SAR-based flood extent map with an exclusion map (EX-map) indicating all areas where the presence of water cannot be derived from SAR intensity observations. In this study, we introduce a methodology for generating an EX-map based on the analysis of time-series of SAR backscatter data. In particular, the identification of the EX-map is based on the combined use of three temporal indicators based on backscatter statistics, i.e. temporal median, minimum and standard deviation. As a test case, EX-maps were derived from Sentinel-1 data acquired during the 2014–2019 time period from six representative study sites. Reference maps were generated using a global land cover map, Digital Elevation Model (DEM)-derived shadow/layover masks, global urban footprint (GUF) data and a Sand Exclusion Layer (SEL). The cross-comparison revealed that the EX-map was consistent with reference maps obtained from other data sources.

REVIEW OF HIGH-RESOLUTION GLOBAL LAND COVER

Abstract. The land cover detection on our planet at high spatial resolution has a key role in many scientific and operational applications, such as climate modeling, natural resources management, biodiversity studies, urbanization analyses and spatial demography. Thanks to the progresses in Remote Sensing, accurate and high-resolution land cover maps have been developed over the last years, aiming at detecting the spatial resolution of different types of surfaces. In this paper we propose a review of the high-resolution global land cover products developed through Earth Observation technologies. A series of general information regarding imagery and data used to produce the map, the procedures employed for the map development and for the map accuracy assessment have been provided for every dataset. The land cover maps described in this paper concern the global distribution of settlements (Global Urban Footprint, Global Human Settlement Built-Up, World Settlement Footprint), water (Global Surface Water), forests (Forest/Non-forest, Tree canopy cover), and a two land cover maps describing world in 10 generic classes (GlobeLand30 and Finer Resolution Observation and Monitoring of Global Land Cover). The advantages and shortcomings of these maps and of the methods employed to produce them are summarized and compared in the conclusions.

GloBiMapsAI: An AI-Enhanced Probabilistic Data Structure for Global Raster Datasets

In the last decade, more and more spatial data has been acquired on a global scale due to satellite missions, social media, and coordinated governmental activities. This observational data suffers from huge storage footprints and makes global analysis challenging. Therefore, many information products have been designed in which observations are turned into global maps showing features such as land cover or land use, often with only a few discrete values and sparse spatial coverage like only within cities. Traditional coding of such data as a raster image becomes challenging due to the sizes of the datasets and spatially non-local access patterns, for example, when labeling social media streams. This article proposes GloBiMap, a randomized data structure, based on Bloom filters, for modeling low-cardinality sparse raster images of excessive sizes in a configurable amount of memory with pure random access operations avoiding costly intermediate decompression. In addition, the data structure is designed to correct the inevitable errors of the randomized layer in order to have a fully exact representation. We show the feasibility of the approach on several real-world datasets including the Global Urban Footprint in which each pixel denotes whether a particular location contains a building at a resolution of roughly 10m globally as well as on a global Twitter sample of more than 220 million precisely geolocated tweets. In addition, we propose the integration of a denoiser engine based on artificial intelligence in order to reduce the amount of error correction information for extremely compressive GloBiMaps.

Built-up area mapping in China from GF-3 SAR imagery based on the framework of deep learning

Timely and reliable acquisition of built-up areas (BAs) information in large regions is of great significance for the assessment of the influence of human activities in local, regional and even global environments and promotes the sustainable development of cities. In this study, a deep learning based framework is presented to automatically extract BAs from Gaofen-3 (GF-3) data in China. In the framework, a U-Net structure based deep learning with convolutional neural network is implemented for semantic segmentation to extract BAs. To overcome the problem of network degradation when the neural network layers become deeper, residual blocks were introduced via identity shortcut connections to add to U-Net. About 30,000 samples containing different distribution types of BAs and corresponding labeled binary images, which are from 27 scenes of GF-3 SAR images covering different regions (for example plain, hilly and mountainous regions) in China, were obtained for model training. With the trained model, more than 1700 scenes of 10-m resolution GF-3 Fine Stripmap II (FSII) mode single-polarized images covering the whole country of China were processed to obtain the BA map of China. On the one hand, quality evaluation based on the confusion matrix was performed to determine the accuracy of the results with the ground truth data, which were manually interpreted by experts. The experimental results show that among 34 provinces or regions, the overall accuracy ranges from 76.05% to 93.45%, and the F1-score ranges from 0.69 and 0.89. On the other hand, the results were also compared with other semantic segmentation methods, such as FCN, PSPNet and U-Net, and other global urban area products, such as the Global Urban Footprint (GUF), Finer Resolution Observation and Monitoring of Global Land Cover (FROM-GLC) and Global Human Settlement Layer (GHSL). The results show the effectiveness of the proposed method.

Mapping Rural Settlements from Landsat and Sentinel Time Series by Integrating Pixel- and Object-Based Methods

Rural settlements account for 45% of the world’s population and are targeted places for poverty eradication. However, compared to urban footprints, the distribution of rural settlements is not well characterized in most existing land use and land cover maps because of their patchy and scattered organization and relative stability over time. In this study, we proposed a pixel- and object-based method to map rural settlements by employing spectral-texture-temporal information from Landsat and Sentinel time series. Spectral indices (maximum normalized difference vegetation index (NDVI) and minimum normalized difference built-up index (NDBI composite) and texture indices (vertical transmit and vertical receive (VV) polarization of mean synthetic aperture radar (SAR) composite) were calculated from all available Landsat and Sentinel-1A data from 1 January 2016 to 31 December 2018. These features were then stacked for segmentation to extract potential rural settlement objects. To better differentiate settlements from bare soil, the gradient of annual NDVI maximum (namely, gradient of change, use gradient for simplicity) from 1 January 1987 to 31 December 2018 was used. The rural training samples were selected from global urban footprint (GUF) products with a post filtering process to remove sample noise. Scatter plots between pixel- and object-based values per feature were delineated by t-distribution ellipses to determine the thresholds. Finally, pixel- and object-based thresholds were applied to four features (NDVI, NDBI, VV, gradient) in Google Earth Engine (GEE) to obtain the distribution of rural settlements in eight selected Asian regions. The derived maps of rural settlements showed consistent accuracy, with a producer’s accuracy (PA) of 0.87, user’s accuracy (UA) of 0.93 and overall accuracy (OA) reaching 90% in different landscape conditions, which are better than existing land cover products.

ERS-1/2 and Sentinel-1 SAR Data Mining for Flood Hazard and Risk Assessment in Lima, Peru

The coastline environment and urban areas of Peru overlooking the Pacific Ocean are among the most affected by El Niño-Southern Oscillation (ENSO) events, and its cascading hazards such as floods, landslides and avalanches. In this work, the complete archives of the European Space Agency (ESA)’s European Remote-Sensing (ERS-1/2) missions and European Commission’s Copernicus Sentinel-1 constellation were screened to select synthetic aperture radar (SAR) images covering the most severe and recent ENSO-related flooding events that affected Lima, the capital and largest city of Peru, in 1997–1998 and 2017–2018. Based on SAR backscatter color composites and ratio maps retrieved from a series of pre-, cross- and post-event SAR pairs, flooded areas were delineated within the Rímac River watershed. These are mostly concentrated along the riverbanks and plain, where low-lying topography and gentle slopes (≤5°), together with the presence of alluvial deposits, also indicate greater susceptibility to flooding. A total of 409 areas (58.50 km2) revealing change were mapped, including 197 changes (32.10 km2) due to flooding-related backscatter variations (flooded areas, increased water flow in the riverbed, and riverbank collapses and damage), and 212 (26.40 km2) due to other processes (e.g., new urban developments, construction of river embankments, other engineering works, vegetation changes). Urban and landscape changes potentially contributing, either detrimentally or beneficially, to flooding susceptibility were identified and considered in the overall assessment of risk. The extent of built-up areas within the basin was mapped by combining information from the 2011 Global Urban Footprint (GUF) produced by the German Aerospace Center (DLR), the Open Street Map (OSM) accessed from the Quantum GIS (QGIS) service, and 2011–2019 very high-resolution optical imagery from Google Earth. The resulting flooding risk map highlights the sectors of potential concern along the Rímac River, should flooding events of equal severity as those captured by SAR images occur in the future.

Spatial Analysis of Settlement Structures to Identify Pattern Formation Mechanisms in Inter-Urban Systems

Dissipative structures known from non-equilibrium thermodynamics can form patterns. Cities are regarded as open, dissipative structures due to their self-organisation and thus in theory are also capable of pattern formation. In a first step to understand similarities between nonlinear pattern formation and inter-urban systems, we investigate how inter-urban structures are arranged. We use data from the Global Urban Footprint to identify spatial regularities in seven regions (Argentina, China, Egypt, France, India, Ghana and USA) and to quantitatively describe settlement patterns by number of objects and density. We find that small areas of the examined data sets show a regular arrangement, the density and number of settlements differ widely between the different regions and the portion of regular areas within this regions strongly correlates with these two parameters. The results can be used to develop mathematical models that describe inter-urban pattern formation on the one hand and to investigate to what extent the respective settlement patterns are related to infrastructural, economic or political boundary conditions on the other.

Towards a Large-Scale 3D Modeling of the Built Environment—Joint Analysis of TanDEM-X, Sentinel-2 and Open Street Map Data

Continental to global scale mapping of the human settlement extent based on earth observation satellite data has made considerable progress. Nevertheless, the current approaches only provide a two-dimensional representation of the built environment. Therewith, a full characterization is restricted in terms of the urban morphology and built-up density, which can only be gained by a detailed examination of the vertical settlement extent. This paper introduces a methodology for the extraction of three-dimensional (3D) information on human settlements by analyzing the digital elevation and radar intensity data collected by the German TanDEM-X satellite mission in combination with multispectral Sentinel-2 imagery and data from the Open Street Map initiative and the Global Urban Footprint human settlement mask. The first module of the underlying processor generates a normalized digital surface model from the TanDEM-X digital elevation model for all regions marked as a built-up area by the Global Urban Footprint. The second module generates a building mask based on a joint processing of Open Street Map, TanDEM-X/TerraSAR-X radar images, the calculated normalized digital surface model and Sentinel-2 imagery. Finally, a third module allocates the local relative heights of the normalized digital surface model to the building structures provided by the building mask. The outcome of the procedure is a 3D map of the built environment showing the estimated local height for all identified vertical building structures at 12 m spatial resolution. The results of a first validation campaign based on reference data collected for the seven cities of Amsterdam (NL), Indianapolis (US), Kigali (RW), Munich (DE), New York (US), Vienna (AT), and Washington (US) indicate the potential of the proposed methodology to accurately estimate the distribution of building heights within the built-up area.

Continental-scale mapping and analysis of 3D building structure

Urban land use is often characterized based on the presence of built-up land, while the land use intensity of different locations is ignored. This narrow focus is at least partially due to a lack of data on the vertical dimension of urban land. The potential of Earth observation data to fill this gap has already been shown, but this has not yet been applied at large spatial scales. This study aims to map urban 3D building structure, i.e. building footprint, height, and volume, for Europe, the US, and China using random forest models. Our models perform well, as indicated by R2 values of 0.90 for building footprint, 0.81 for building height, and 0.88 for building volume, for all three case regions combined. In our multidimensional input variables, we find that built-up density derived from the Global Urban Footprint (GUF) is the most important variable for estimating building footprint, while backscatter intensity of Synthetic Aperture Radar (SAR) is the most important variable for estimating building height. A combination of the two is essential to estimate building volume. Our analysis further highlights the heterogeneity of 3D building structure across space. Specifically, buildings in China tend to be taller on average (10.35 m) compared to Europe (7.37 m) and the US (6.69 m). At the same time, the building volume per capita in China is lowest, with 302.3 m3 per capita, while Europe and the US show estimates of 404.6 m3 and 565.4 m3, respectively. The results of this study (3D building structure data for Europe, the US, and China) are publicly available, and can be used for further analysis of urban environment, spatial planning, and land use projections.

Urbanization that hides in the dark – Spotting China’s “ghost neighborhoods” from space

The excessive planning and construction of new cities and towns as well the massive expansion of planned settlement areas in China reveals a surprising phenomenon: extensive newly constructed areas that are characterized by severe under capacity. This phenomenon is described as “ghost city” and usually appears at the intra-urban scale. However, current data and methods are inappropriate to detect this phenomenon at this intra-urban scale for entire China. Besides, most studies neglect the usual immigration period of newly built areas. In this study, we suggest a new term, the “ghost neighborhood”, which refers to a new residential neighborhood that still runs at severe under capacity after a typical immigration period of five to six years. Based on this conceptual baseline, we identify “ghost neighborhoods” across China by developing a methodology to gradually exclude the “non-ghost areas” by predominantly relying on night-light emissions. The process is based on LJ1-01 night-light data, TanDEM-X data and Global Urban Footprint data, which feature evident advantages in spatial resolutions while being available for entire China. The main results are as follows: 1) we identify 1048 “ghost neighborhoods” across the urban landscapes of China with a total coverage area of 353.64 km2; they are estimated to have the capacity to house 13.6 million people; 2) the identified “ghost neighborhoods” are particularly frequent and large in the dynamically urbanizing regions, such as the Pearl River Delta, the Yangtze River Delta and the entire Shandong province.

Archaeological Sites in Small Towns—A Sustainability Assessment of Northumberland County

Background: Land-use change, resulting from urban expansion, has led to the unprecedented importance of rethinking our interactions with the environment in general. The consequences of anthropogenic activity on natural land, as well as sustainable land use types, is a significant concern to the detriment of small towns. In this article, I assess the changes in the urban footprint morphology of Northumberland County while evaluating the consequences of urban fragmentation on archaeological sites. Northumberland has a total population of 85,598 divided into seven municipalities and the Alderville First Nation reserve. Northumberland County is a commuting distance of one hour from Toronto, one of North America’s most important economic hubs. It is of the highest importance to measure and understand the current characteristics of land-use change and its impacts on the sustainability of small towns and local communities. Methods: A total of archaeological sites are used to test the impacts of urbanization and land-use change throughout the perimeter of Northumberland county. Spatial analysis is conducted employing spatial autocorrelation techniques (global and local) to assess the existence of hotspots of archaeological sites and the potential endangerment of these sites due to urban pressure. A hexagonal topology is used to derive a regional understanding of impacted regions within the county, allowing the richness of archaeological heritage sites as well as the possible concerns of these spatial clusters to be examined. Results: It is shown that a significant number of archaeological sites are present in Northumberland county, with a clear indication of a hotspot around Rice Lake. Future urbanization as well as land-use fragmentation may have an impact on the sustainability of these archaeological sites. It is shown that hexagonal geometries may establish relevant spatial surfaces to compare urban and archaeological sites as criteria to understand the endangerment of archaeological sites. The availability of the Global Urban Footprint (GUF) creates a particularly rigorous framework for the detection of impact on archaeological sites. Conclusion: The findings suggest that archaeological sites in small towns can benefit from high-resolution urban land-use footprints to detect impacts on archaeological sites and the relations of land-use change and intrasite management. This is of utmost importance in times of significant anthropogenic change brought by increasing urban pressure on small and medium-sized towns.

Using Multi-Sensor Satellite Images and Auxiliary Data in Updating and Assessing the Accuracies of Urban Land Products in Different Landscape Patterns

Rapid and accurate updating of urban land areas is of great significance to the study of environmental changes. Although there are many urban land products (ULPs) at present, such as GlobeLand30, Global Urban Footprint (GUF), and Global Human Settlement Layer (GHSL), these products are all static data of a certain year, and are not able to provide high-accuracy updating of urban land areas. In addition, the accuracies of these data and their application value in the update of urban land areas need to be urgently proven. Therefore, we proposed an approach to quickly and accurately update urban land areas in the Kuala Lumpur region of Malaysia, and assessed the accuracies of urban land products in different urban landscape patterns. The approach combined the advantages of multi-source data including existing ULPs, OpenStreetMap (OSM) data, Landsat Operational Land Imager (OLI), and Phased Array type L-band Synthetic Aperture Radar (PALSAR) images. Three main steps make up this approach. First, the urban land training samples were selected in the urban areas consistent with GlobeLand30, GUF, and GHSL, and samples of bare land, vegetation, water bodies, and road auxiliary data were obtained by GlobeLand30 and OSM. Then, the random forest was used to extract urban land areas according to the object’s features in the OLI and PALSAR images. Last, we assessed the accuracies of GlobeLand30, GUF, GHSL, and the results of this study (ULC) by using point and area validation methods. The results showed that the ULC had the highest overall accuracy of 90.18% among the four products and could accurately depict urban land in different urban landscapes. The GHSL was the second most accurate of the four products, and the accuracy in urban areas was much higher than that in rural areas. The GUF had many omission errors in urban land areas and could not delineate a large area of complete spatial information of urban land, but it could effectively extract scattered residential land with small patches. GlobeLand30 had the lowest accuracy and could only express rough, large-scale urban land. The above conclusions provide evidence that ULPs and the approach proposed in this study have a great application potential for high-accuracy updating of urban land areas.

A new ranking of the world's largest cities—Do administrative units obscure morphological realities?

With 37 million inhabitants, Tokyo is the world's largest city in UN statistics. With this work we call this ranking into question. Usually, global city rankings are based on nationally collected population figures, which rely on administrative units. Sprawling urban growth, however, leads to morphological city extents that may surpass conventional administrative units. In order to detect spatial discrepancies between the physical and the administrative city, we present a methodology for delimiting Morphological Urban Areas (MUAs). We understand MUAs as a territorially contiguous settlement area that can be distinguished from low-density peripheral and rural hinterlands. We design a settlement index composed of three indicators (settlement area, settlement area proportion and density within the settlements) describing a gradient of built-up density from the urban center to the periphery applying a sectoral monocentric city model. We assume that the urban-rural transition can be defined along this gradient. With it, we re-territorialize the conventional administrative units. Our data basis are recent mapping products derived from multi-sensoral Earth observation (EO) data – namely the Global Urban Footprint (GUF) and the GUF Density (GUF-DenS) – providing globally consistent knowledge about settlement locations and densities. For the re-territorialized MUAs we calculate population numbers using WorldPop data. Overall, we cover the 1692 cities with >300,000 inhabitants on our planet. In our results we compare the consistently re-territorialized MUAs and the administrative units as well as their related population figures. We find the MUA in the Pearl River Delta the largest morphologically contiguous urban agglomeration in the world with a calculated population of 42.6 million. Tokyo, in this new list ranked number 2, loses its top position. In rank-size distributions we present the resulting deviations from previous city rankings. Although many MUAs outperform administrative units by area, we find that, contrary to what we assumed, in most cases MUAs are considerably smaller than administrative units. Only in Europe we find MUAs largely outweighing administrative units in extent.