Global Urban Mapping Research Articles

Global urban high-resolution land-use mapping: From benchmarks to multi-megacity applications

Timely and reliable land-use maps are of great importance in urban planning and environmental monitoring. With the rich spatial structure information, very high resolution (VHR) imagery is an important data source for identifying complex urban land use. However, the existing scene datasets and land-use mapping products based on VHR images have the following three problems: 1) accurate geographic boundaries of urban land parcels are lacking; 2) the category systems are inconsistent with the definitions in urban land use; and 3) it is difficult to achieve efficient and fully automated mapping in multiple cities. To tackle these problems, the GlobalUrbanNet-based automatic multi-city mapping and analysis (GAMMA) framework is proposed in this article. The GAMMA framework is made up of the GlobalUrbanNet (GUN) dataset, the multi-city fully automatic urban land-use mapping (AutoULUM) method, and the analysis of urban development patterns. Specifically, the large-scale 42-category fine-grained VHR urban land-use dataset—the GUN dataset—was constructed to deal with the above global urban land-use mapping problems, which contains 1,846,151 samples and 42 land-use categories covering six continents. The GUN dataset samples with land-use semantics and parcel boundaries were generated automatically based on the open-source area of interest (AOI) data from OpenStreetMap (OSM). In addition, the AutoULUM method is proposed to automate the process of OSM road network rectification and land parcel generation. On this basis, efficient and complete multi-city land-use maps can be produced using the GUN-pretrained scene classification models. To establish a benchmark for urban land-use classification, the representative urban land-use classification methods were evaluated on the GUN dataset. For further application, eight megacities from six continents were selected for automatic land-use mapping and analysis, i.e., Shanghai, Wuhan, and Chengdu in Asia, Helsinki in Europe, Nairobi in Africa, New York in North America, Rio de Janeiro in South America, and Sydney in Oceania. The results show that the models trained on the proposed GUN dataset have good generalizability in global urban areas, the AutoULUM method achieves efficient and fully automatic land-use mapping, and the GAMMA framework will help boost the coordinated development of multiple cities around the world.

Assessing Multi-Temporal Global Urban Land-Cover Products Using Spatio-Temporal Stratified Sampling

In recent years, the availability of multi-temporal global land-cover datasets has meant that they have become a key data source for evaluating land cover in many applications. Due to the high data volume of the multi-temporal land-cover datasets, probability sampling is an efficient method for validating multi-temporal global urban land-cover maps. However, the current accuracy assessment methods often work for a single-epoch dataset, and they are not suitable for multi-temporal data products. Limitations such as repeated sampling and inappropriate sample allocation can lead to inaccurate evaluation results. In this study, we propose the use of spatio-temporal stratified sampling to assess thematic mappings with respect to the temporal changes and spatial clustering. The total number of samples in the two stages, i.e., map and pixel, was obtained by using a probability sampling model. Since the proportion of the area labeled as no change is large while that of the area labeled as change is small, an optimization algorithm for determining the sample sizes of the different strata is proposed by minimizing the sum of variance of the user’s accuracy, producer’s accuracy, and proportion of area for all strata. The experimental results show that the allocation of sample size by the proposed method results in the smallest bias in the estimated accuracy, compared with the conventional sample allocation, i.e., equal allocation and proportional allocation. The proposed method was applied to multi-temporal global urban land-cover maps from 2000 to 2010, with a time interval of 5 years. Due to the spatial aggregation characteristics, the local pivotal method (LPM) is adopted to realize spatially balanced sampling, leading to more representative samples for each stratum in the spatial domain. The main contribution of our research is the proposed spatio-temporal sampling approach and the accuracy assessment conducted for the multi-temporal global urban land-cover product.

An updated MODIS global urban extent product (MGUP) from 2001 to 2018 based on an automated mapping approach

Timely and accurate global urban maps are fundamental in monitoring urbanization process and understanding environmental degradation. Therefore, this paper proposed a locally adaptive and fully automated global mapping method and produced an updated 250 m MODIS global urban area product (MGUP) from 2001 to 2018. The proposed approach mainly consists of 1) automated samples extraction from existing global products, 2) locally adaptive samples selection and trained classification in each 5° × 5° grid, and 3) post-processing in terms of the spatio-temporal context. To validate the product, 9 groups of samples for every two years from 2001 to 2018, amounting to over 150,000 sample points, were collected manually from Landsat imagery as global validation dataset. Accuracy assessment indicates that MGUP has a F-score of 0.88, achieving better results than the contemporary global products, i.e., MCD12Q1.v5 (0.82), MCD12Q1.v6 (0.86), and CCI-LC (0.86). Analysis of urban expansion based on MGUP shows that the world’s urban area increased to 802233 km2 and accounted for 0.54% of the Earth’s land surface in 2018. The total global urban area expanded by 1.68 times from 2001 to 2018. At continent level, urban density varies considerably, and the highest and lowest one is in Europe (1.78%) and Oceania (0.15%), respectively. At national level, large increment of urban area mainly occurs in North America, Asia, and South America; and countries having high growth rates are mainly developing countries in Africa and Asia. MGUP can be downloaded at https://www.researchgate.net/publication/339873537_MGUP_annual_global_2001_2018.

A global record of annual urban dynamics (1992–2013) from nighttime lights

The nighttime light (NTL) observations from Defense Meteorological Satellite Program/Operational Linescane System (DMSP/OLS) offer great potentials to study urban dynamics from regional to global scales, for more than two decades. In this paper, we presented a new approach to develop spatially and temporally consistent global urban maps from 1992 to 2013, using the DMSP/OLS NTL observations. First, potential urban clusters were delineated using the NTL data and a segmentation method. Then, a quantile-based approach was used to remove rural and suburban areas sequentially in the potential urban clusters. Finally, the derived series of urban extents in the entire study period (1992–2013) were improved for temporal consistency. We found the percentage of global urban areas relative to the world's land surface area increased from 0.23% in 1992 to 0.53% in 2013. Asia is the continent with the most significant urban growth, worldwide. The time series of global urban maps were evaluated for the spatial agreement and temporal consistency using a variety of widely used independent land-cover products. This evaluation indicates that the proposed approach is robust and performs well in deriving global urban dynamics across different spatial scales, i.e., cluster, province (or state), country, and region. Moreover, this quantile-based approach is advantageous, compared with other methods used in previous studies, because it does not require additional data for enhancement or calibration. The new time series of urban maps from this study offer a new dataset for studying global urbanization during the past decades and unique information to explore potential future trajectories of urban development, which appears to be distinct for different nations/regions, globally. Such information is pre-requisite for achieving the sustainable development goals, and associated targets, during ensuing decades.

A Bayesian approach to mapping the uncertainties of global urban lands

Global distribution of urban lands is one of the essential pieces of information necessary for urban planning. However, large disagreement exists among different products and the uncertainty remains difficult to quantify. We applied a Bayesian approach to map the uncertainties of global urban lands. We demonstrated the approach by producing a hybrid global urban land map that synthesized five different urban land maps in ca. 2000 at 1-km resolution. The resulting hybrid map is a posterior probability map with pixel values suggesting the probability of being urban land, which is validated by 30-m higher resolution references. We also quantified the minimum and maximum urban areas in 2000 for each country/continent based on subjective probability thresholds (i.e., 0.9 and 0.1) on our hybrid urban map. Globally, we estimated that the urban land area was between 377,000 and 533,000 km2 in 2000. The credible interval of minimum/maximum urban area can help guide future studies in estimating urban areas. In addition to providing uncertainty information, the hybrid map also achieves higher accuracy than individual maps when it is converted into a binary urban/non-urban map using a probability threshold of 0.5. This new method has the ability to further integrate discrete site/location-based data, local, regional, and global urban land maps. As more data is sequentially integrated, the accuracy is expected to improve. Therefore, our hybrid map should not be regarded as a final product, but a new prior product for future synthesis and integration toward a “big data” solution.

Supervised Classification of Built-Up Areas in Sub-Saharan African Cities Using Landsat Imagery and OpenStreetMap

The Landsat archives have been made freely available in 2008, allowing the production of high resolution built-up maps at the regional or global scale. In this context, most of the classification algorithms rely on supervised learning to tackle the heterogeneity of the urban environments. However, at a large scale, the process of collecting training samples becomes a huge project in itself. This leads to a growing interest from the remote sensing community toward Volunteered Geographic Information (VGI) projects such as OpenStreetMap (OSM). Despite the spatial heterogeneity of its contribution patterns, OSM provides an increasing amount of information on the earth’s surface. More interestingly, the community has moved beyond street mapping to collect a wider range of spatial data such as building footprints, land use, or points of interest. In this paper, we propose a classification method that makes use of OSM to automatically collect training samples for supervised learning of built-up areas. To take into account a wide range of potential issues, the approach is assessed in ten Sub-Saharan African urban areas from various demographic profiles and climates. The obtained results are compared with: (1) existing high resolution global urban maps such as the Global Human Settlement Layer (GHSL) or the Human Built-up and Settlements Extent (HBASE); and (2) a supervised classification based on manually digitized training samples. The results suggest that automated supervised classifications based on OSM can provide performances similar to manual approaches, provided that OSM training samples are sufficiently available and correctly pre-processed. Moreover, the proposed method could reach better results in the near future, given the increasing amount and variety of information in the OSM database.

High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform

Timely and accurate delineation of global urban land is fundamental to the understanding of global environmental changes. However, most of the contemporary global urban land maps have coarse resolutions and are available for one or two years only. In this study, we developed the multi-temporal global urban land maps based on Landsat images for the 1990–2010 period with a five-year interval (‘Urban land’ in these maps refers to ‘impervious surface’, i.e., artificial cover and structures such as pavement, concrete, brick, stone and other man-made impenetrable cover types). We proposed the method of Normalized Urban Areas Composite Index (NUACI) and utilized the Google Earth Engine to facilitate the global urban land classifications from an extensive number of Landsat images. The global level's overall accuracy, producer's accuracy and user's accuracy for our mapping results are 0.81–0.84, 0.50–0.60 and 0.49–0.61, respectively. The Kappa values are 0.43–0.50 at the global level, and ~0.33 (in China) and ~0.42 (in the U.S.) at the country level. By analyzing the presented dataset, we found that the world's urban land area had increased from 450.97 ± 1.18 thousand km2 in 1990 to 747.05 ± 1.50 thousand km2 in 2010, reaching a global coverage of 0.63%. China, the United States and India together (14% of the world's terrestrial area in total) contributed almost 43% of the total increase of global urban land area. A free download link for these data is attached at the end of this paper.

Spatiotemporally enhancing time-series DMSP/OLS nighttime light imagery for assessing large-scale urban dynamics

Accurate, up-to-date, and consistent information of urban extents is vital for numerous applications central to urban planning, ecosystem management, and environmental assessment and monitoring. However, current large-scale urban extent products are not uniform with respect to definition, spatial resolution, temporal frequency, and thematic representation. This study aimed to enhance, spatiotemporally, time-series DMSP/OLS nighttime light (NTL) data for detecting large-scale urban changes. The enhanced NTL time series from 1992 to 2013 were firstly generated by implementing global inter-calibration, vegetation-based spatial adjustment, and urban archetype-based temporal modification. The dataset was then used for updating and backdating urban changes for the contiguous U.S.A. (CONUS) and China by using the Object-based Urban Thresholding method (i.e., NTL-OUT method, Xie and Weng, 2016b). The results showed that the updated urban extents were reasonably accurate, with city-scale RMSE (root mean square error) of 27km2 and Kappa of 0.65 for CONUS, and 55km2 and 0.59 for China, respectively. The backdated urban extents yielded similar accuracy, with RMSE of 23km2 and Kappa of 0.63 in CONUS, while 60km2 and 0.60 in China. The accuracy assessment further revealed that the spatial enhancement greatly improved the accuracy of urban updating and backdating by significantly reducing RMSE and slightly increasing Kappa values. The temporal enhancement also reduced RMSE, and improved the spatial consistency between estimated and reference urban extents. Although the utilization of enhanced NTL data successfully detected urban size change, relatively low locational accuracy of the detected urban changes was observed. It is suggested that the proposed methodology would be more effective for updating and backdating global urban maps if further fusion of NTL data with higher spatial resolution imagery was implemented.

Urban mapping using DMSP/OLS stable night-time light: a review

ABSTRACTThe Defense Meteorological Satellite Program/Operational Linescane System (DMSP/OLS) stable night-time light (NTL) data showed great potential in urban extent mapping across a variety of scales with historical records dating back to 1990s. In order to advance this data, a systematic methodology review on NTL-based urban extent mapping was carried out, with emphases on four aspects including the saturation of luminosity, the blooming effect, the intercalibration of time series, and their temporal pattern adjustment. We think ancillary features (e.g. land surface conditions and socioeconomic activities) can help reveal more spatial details in urban core regions with high digital number (DN) values. In addition, dynamic optimal thresholds are needed to address issues of different exaggeration of NTL data in the large scale urban mapping. Then, we reviewed three key aspects (reference region, reference satellite/year, and calibration model) in the current intercalibration framework of NTL time series, and summarized major reference regions in literature that were used for intercalibration, which is critical to achieve a globally consistent series of NTL DN values over years. Moreover, adjustment of temporal pattern on intercalibrated NTL series is needed to trace the urban sprawl process, particularly in rapidly developing regions. In addition, we analysed those applications for urban extent mapping based on the new generation NTL data of Visible/Infrared Imager/Radiometer Suite. Finally, we prospected the challenges and opportunities including the improvement of temporally inconsistent NTL series, mitigation of spatial heterogeneity of blooming effect in NTL, and synthesis of different NTL satellites, in global urban extent mapping.

New urban map of Eurasia using MODIS and multi-source geospatial data

Urban areas are of paramount significance to both the individuals and communities at local and regional scales. However, the rapid growth of urban areas exerts effects on climate, biodiversity, hydrology, and natural ecosystems worldwide. Therefore, regular and up-to-date information related to urban extent is necessary to monitor the impacts of urban areas at local, regional, and potentially global scales. This study presents a new urban map of Eurasia at 500 m resolution using multi-source geospatial data, including Moderate Resolution Imaging Spectroradiometer (MODIS) data of 2013, population density of 2012, the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) nighttime lights of 2012, and constructed Impervious Surface Area (ISA) data of 2010. The Eurasian urban map was created using the threshold method for these data, combined with references of fine resolution Landsat and Google Earth imagery. The resultant map was compared with nine global urban maps and was validated using random sampling method. Results of the accuracy assessment showed high overall accuracy of the new urban map of 94%. This urban map is one product of the 20 land cover classes of the next version of Global Land Cover by National Mapping Organizations.

A Global Inventory of Urban Corridors Based on Perceptions and Night-Time Light Imagery

The massive growth of some urban areas has led to new constellations of urban forms. New concepts describing large urban areas have been introduced but are not always defined and mapped sufficiently and consistently. This article describes urban corridors as an example of such a concept with an ambiguous spatial definition. Based on the existing usage of the concept in scientific literature and the results of a questionnaire, we attempt to spatially parameterize and identify the main characteristics of urban corridors on a global scale. The parameters we use are physically measurable and therefore serve as a basis for a harmonized and scientifically sound mapping of urban corridors using remote sensing data and methods. Our results are presented in a global urban corridor map.

Global mapping of urban built-up areas of year 2014 by combining MODIS multispectral data with VIIRS nighttime light data

ABSTRACTAn improved methodology for the extraction and mapping of urban built-up areas at a global scale is presented in this study. The Moderate Resolution Imaging Spectroradiometer (MODIS)-based multispectral data were combined with the Visible Infrared Imager Radiometer Suite (VIIRS)-based nighttime light (NTL) data for robust extraction and mapping of urban built-up areas. The MODIS-based newly proposed Urban Built-up Index (UBI) was combined with NTL data, and the resulting Enhanced UBI (EUBI) was used as a single master image for global extraction of urban built-up areas. Due to higher variation of the EUBI with respect to geographical regions, a region-specific threshold approach was used to extract urban built-up areas. This research provided 500-m-resolution global urban built-up map of year 2014. The resulted map was compared with three existing moderate-resolution global maps and one high-resolution map in the United States. The comparative analysis demonstrated finer details of the urban built-up cover estimated by the resultant map.

Identifying hotspots of human anthrax transmission using three local clustering techniques

This study compared three local cluster detection methods to identify local hotspots of human cutaneous anthrax (HCA) transmission in the country of Georgia where cases have been steadily increasing since the dissolution of the Soviet Union. Recent reports have indicated that the disease has reached historical levels in 2012 highlighting the need for better informed policy recommendations and targeted control measures. The purpose of this paper was to identify spatial clusters of HCA to aid in the implementation of targeted public health interventions. At the same time, we compared the utility of different statistical tests in identifying hotspots. We used the Getis-Ord (Gi∗(d)), a multidirectional optimal ecotope-based algorithm (AMOEBA) – a cluster morphology statistic, and the spatial scan statistic in SaTScan™. Data on HCA cases from 2000 to 2012 at the community level were aggregated to an 8 × 8 km grid surface and population data from the Global Rural and Urban Mapping Project (GRUMP) were used to calculate local incidence. In general, there was agreement between tests in the locations of HCA hotspots. Significant local clusters of high HCA incidence were identified in the southern, eastern and western regions of Georgia. The Gi∗(d) and spatial scan statistics appeared more sensitive but less specific than the AMOEBA algorithm. The scan statistic identified larger geographic areas as hotspots of transmission. In general, the spatial scan statistic and Gi∗(d) performed well for spatial clusters with lower incidence rates, whereas AMOEBA was well suited for defining local spatial clusters of higher HCA incidence. In resource constrained areas, efficient allocation of public health interventions is crucial. Our findings identified hotspots of HCA that can be used to target public health interventions such as livestock vaccination and training on proper outbreak management. This paper illustrates the benefits of evaluating statistical approaches for defining disease hotspots and highlights differences in these clustering approaches applicable beyond public health studies.

Unsupervised Global Urban Area Mapping via Automatic Labeling from ASTER and PALSAR Satellite Images

In this study, a novel unsupervised method for global urban area mapping is proposed. Different from traditional clustering-based unsupervised methods, in our approach a labeler is designed, which is able to automatically select training samples from satellite images by propagating common urban/non-urban knowledge through the unlabeled data. Two kinds of satellite images, captured by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and the Phased Array L-band Synthetic Aperture Radar (PALSAR), are exploited here. In this method, spectral features are first extracted from the original dataset, followed by coarse prediction of urban/non-urban areas via weak classifiers. By developing an improved belief-propagation based clustering algorithm, a confidence map is obtained and training data are selected via weighted sampling. Finally, the urban area map is obtained by employing the Support Vector Machine (SVM) classifier. The proposed method can generate urban areamaps at a resolution of 15 m, while the same settings are used for all test cases. Experimental results involving 75 scenes from different climate zones show that our proposed method achieves an overall accuracy of 84.4% and a kappa coefficient of 0.628, which is competitive relative to the supervised SVM method.

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) after fifteen years: Review of global products

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is a 15-channel imaging instrument operating on NASA’s Terra satellite. A joint project between the U.S. National Aeronautics and Space Administration and Japan’s Ministry of Economy, Trade, and Industry, ASTER has been acquiring data for 15 years, since March 2000. The archive now contains over 2.8 million scenes; for the majority of them, a stereo pair was collected using nadir and backward telescopes imaging in the NIR wavelength. The majority of users require only a few to a few dozen scenes for their work. Studies have ranged over numerous scientific disciplines, and many practical applications have benefited from ASTER’s unique data. A few researchers have been able to mine the entire ASTER archive, that is now global in extent due to the long duration of the mission. Six examples of global products are described in this contribution: the ASTER Global Digital Elevation Model (GDEM), the most complete, highest resolution DEM available to all users; the ASTER Emissivity Database (ASTER GED), a global 5-band emissivity map of the land surface; the ASTER Global Urban Area Map (AGURAM), a 15-m resolution database of over 3500 cities; the ASTER Volcano Archive (AVA), an archive of over 1500 active volcanoes; ASTER Geoscience products of the continent of Australia; and the Global Ice Monitoring from Space (GLIMS) project.

Spaceborne SAR data for global urban mapping at 30 m resolution using a robust urban extractor

With more than half of the world population now living in cities and 1.4 billion more people expected to move into cities by 2030, urban areas pose significant challenges on local, regional and global environment. Timely and accurate information on spatial distributions and temporal changes of urban areas are therefore needed to support sustainable development and environmental change research. The objective of this research is to evaluate spaceborne SAR data for improved global urban mapping using a robust processing chain, the KTH-Pavia Urban Extractor. The proposed processing chain includes urban extraction based on spatial indices and Grey Level Co-occurrence Matrix (GLCM) textures, an existing method and several improvements i.e., SAR data preprocessing, enhancement, and post-processing. ENVISAT Advanced Synthetic Aperture Radar (ASAR) C-VV data at 30m resolution were selected over 10 global cities and a rural area from six continents to demonstrate the robustness of the improved method. The results show that the KTH-Pavia Urban Extractor is effective in extracting urban areas and small towns from ENVISAT ASAR data and built-up areas can be mapped at 30m resolution with very good accuracy using only one or two SAR images. These findings indicate that operational global urban mapping is possible with spaceborne SAR data, especially with the launch of Sentinel-1 that provides SAR data with global coverage, operational reliability and quick data delivery.

An Automated Method for Global Urban Area Mapping by Integrating ASTER Satellite Images and GIS Data

We present an automated classification method for global urban area mapping by integrating satellite images taken by Visible and Near-Infrared Radiometer of Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER/VNIR) and GIS data derived from existing urban area maps. The method consists of two steps. First, we extracted urban areas from ASTER/VNIR satellite images by using an iterative machine-learning classification method known as Learning with Local and Global Consistency (LLGC). This method is capable of automatically performing classification with a noisy training dataset, in our case, low-resolution urban maps. Therefore, we were able to perform supervised classification of ASTER/VNIR images without using labor-intensive visual interpretation. Second, we integrated the LLGC confidence map with other maps by logistic regression. The logistic regression complemented misclassifications in the LLGC map and provided useful information for further improvement of the model. In an experiment including 194 scenes of ASTER/VNIR images, the integrated maps were developed at a resolution of 15 m resolution, which is much finer than existing maps with resolutions of 300 to 1000 m. The maps achieved an overall accuracy of 90.0% and a kappa coefficient of 0.565, both of which are higher than or almost equal to the values for major existing global urban area maps.

Monitoring urbanization dynamics in India using DMSP/OLS night time lights and SPOT-VGT data

India is a rapidly urbanizing country and has experienced profound changes in the spatial structure of urban areas. This study endeavours to illuminate the process of urbanization in India using Defence Meteorological Satellites Program – Operational Linescan System (DMSP-OLS) night time lights (NTLs) and SPOT vegetation (VGT) dataset for the period 1998–2008. Satellite imagery of NTLs provides an efficient way to map urban areas at global and national scales. DMSP/OLS dataset however lacks continuity and comparability; hence the dataset was first intercalibrated using second order polynomial regression equation. The intercalibrated dataset along with SPOT-VGT dataset for the year 1998 and 2008 were subjected to a support vector machine (SVM) method to extract urban areas. SVM is semi-automated technique that overcomes the problems associated with the thresholding methods for NTLs data and hence enables for regional and national scale assessment of urbanization. The extracted urban areas were validated with Google Earth images and global urban extent maps. Spatial metrics were calculated and analyzed state-wise to understand the dynamism of urban areas in India. Significant changes in urban proportion were observed in Tamil Nadu, Punjab and Kerala while other states also showed a high degree of changes in area wise urban proportion.

Development of a New Ground Truth Database for Global Urban Area Mapping from a Gazetteer

We developed a ground truth database for urban areas from the Global Rural-Urban Mapping Project (GRUMP) Settlement Points gazetteer of populated place names by visually interpreting 3,734 urban points on satellite images, thus acquiring 2,144 urban and 1,388 non-urban data points. Our database contained many more urban data points than the existing databases, which had only 0 to 11 ground truth data points. We used our database in combination with the Degree Confluence Project database to assess the accuracy of eight satellite-derived urban area maps, among which the MODIS Terra + Aqua Land Cover Type Yearly L3 Global 500 m SIN Grid was the most accurate (84% overall accuracy; kappa coefficient, 0.63). Moreover, the most recently published maps were not necessarily the most accurate. We compared the accuracy assessment results of our database with those of another database and found that ours detected more errors of commission but included less chance agreement.

Mapping urban areas on a global scale: which of the eight maps now available is more accurate?

Eight groups from government and academia have created 10 global maps that offer a ca 2000 portrait of land in urban use. Our initial investigation found that their estimates of the total amount of urban land differ by as much as an order of magnitude (0.27–3.52 ×106 km2). Since it is not possible for these heterogeneous maps to all represent urban areas accurately, we undertake the first global accuracy assessment of these maps using a two-tiered approach that draws on a stratified random sample of 10 000 high-resolution Google Earth validation sites and 140 medium-resolution Landsat-based city maps. Employing a wide range of accuracy measures at different spatial scales, we conclude that the new MODIS 500 m resolution global urban map has the highest accuracy, followed by a thresholded version of the Global Impervious Surface Area map based on the Night-time Lights and LandScan datasets.