Dataset of Global Urban Boundaries, Impervious Surface Area and Green Space (2000, 2010, 2020)
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DOI:10.3974/geodb.2025.01.04.V1
Published:Jan. 2025
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Key Words：
urban expansion,land cover,impervious surface area,green space,remote sensing,
Abstract：
To objectively and accurately characterize surface conditions under human urban and rural construction activities and achieve global monitoring of urban expansion and land cover changes using remote sensing, a principle and methodology for hierarchical-scale mapping of urban surface structural components were developed. Landsat TM/ETM /OLI served as the primary data source, supplemented by HJ-1A/B, GF, ZY-3, DMSP/OLS, and NPP/VIIRS datasets. The methodology leveraged the Google Earth Engine platform and machine learning techniques to create a dataset of global urban boundaries, impervious surface area and green space (2000, 2010, 2020). Data accuracy was assessed using high-resolution remote sensing imagery, demonstrating an average mapping accuracy of 91.20% for global urban boundaries. Validation of impervious surface mapping yielded an average correlation coefficient (R) of 0.92 and a root mean square error (RMSE) of 13%. Similarly, validation of urban green space mapping showed an average R of 0.91 and an RMSE of 13%. The dataset includes the following data in each continent in 2000, 2010 and 2020: (1) urban boundary data; (2) impervious surface data; and (3) urban green space data. The dataset has a spatial resolution of 250 mx250 m, is archived in .tif and .txt formats, and consists of 55 data files with data size of 3.25 GB (Compressed into one file with 72.8 MB). The research paper based on the dataset has been published in Science Bulletin, Volume 66, Issue 4, 2021.
Foundation Item：
Ministry of Science and Technology of P. R. China (1061302600001, 2019QZKK0608); National Natural Science Foundation of China (41871343); Chinese Academy of Sciences (XDA23100201)
Data Citation：
1. Dataset of Global Urban Boundaries, Impervious Surface Area and Green Space (2000, 2010, 2020)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2025. https://doi.org/10.3974/geodb.2025.01.04.V1.
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