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Data Details

Experimental Dataset of Urban Built-up Area Boundaries in China Derived from the POI & ISA Composite Index (2015, 2020, 2025)


LAN Kai1LI Jinglei1YUE Dengzhao1HUANG Chenyang1YANG Chengchao1ZHANG Yang*1,2
1 School of Geography and Planning，Chengdu University of Technology，Chengdu 610059，China2 Research Center for Human Geography of the Qingzang Plateau and Its Eastern Margin，Chengdu University of Technology，Chengdu 610059，China

DOI:10.3974/geodb.2026.08.02.V1

Published:Aug. 2026

Visitors：37       Data Files Downloaded：1      
Data Downloaded：2.64 MB      Citations：

Key Words：

Urban built-up area,POI & ISA composite index,Points of Interest (POI),Impervious surface area (ISA)

Abstract：

Urban built-up areas can provide essential information for urban planning, development, and management, and they reflect urbanization processes and spatial patterns. It is important for monitoring urban space and assessing sustainable development to identify the built-up area boundaries. The Experimental Dataset of Urban Built-up Area Boundaries in China Derived from the POI & ISA Composite Index (2015, 2020, 2025) was developed based on Landsat OLI/OLI-2 images and Point-of-interest (POI) data from Amap in 2015, 2020, and 2025. Surface-cover characteristics and urban functional attributes were integrated into a POI & ISA composite index. Threshold segmentation and spatial post-processing were then applied to extract the experimental urban built-up area boundaries for 41 cities in China in 2015, 2020, and 2025. The dataset contains the experimental built-up area boundaries and their corresponding area statistics for 41 cities in each of 2015, 2020, and 2025. The dataset is archived in .shp and .xlsx data formats, and consists of 985 data files with data size of 5.05 MB (Compressed into one file with 2.63 MB).

Foundation Item：

National Natural Science Foundation of China (42501258)

Data Citation：

LAN Kai, LI Jinglei, YUE Dengzhao, HUANG Chenyang, YANG Chengchao, ZHANG Yang*. Experimental Dataset of Urban Built-up Area Boundaries in China Derived from the POI & ISA Composite Index (2015, 2020, 2025)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2026. https://doi.org/10.3974/geodb.2026.08.02.V1.
.

References：


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     [2] Wang, L., Li, C. C., Ying, Q., et al. China’s urban expansion from 1990 to 2010 determined with satellite remote sensing [J]. Chinese Science Bulletin, 2012, 57(22): 2802-2812.
     [3] Jiang, H. P., Sun, Z. C., Guo, H. D., et al. A standardized dataset of built-up areas of China’s cities with populations over 300,000 for the period 1990-2015 [J]. Big Earth Data, 2022, 6(1): 103-126.
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     [5] Zhu, Y. L., Zhang, Y., Yang, R. Z., et al. Dataset of built-up areas in the Chengdu-Chongqing Economic Circle based on the POI&ISA composite index (2010, 2020) [J]. Journal of Global Change Data & Discovery, 2023, 7(4): 382-390.
     [6] Schneider, A., Friedl, M. A., McIver, D. K., et al. Mapping urban areas by fusing multiple sources of coarse resolution remotely sensed data [J]. Photogrammetric Engineering & Remote Sensing, 2003, 69(12): 1377-1386.
     [7] Bartholomé, E., Belward, A. S. GLC2000: A new approach to global land cover mapping from Earth observation data [J]. International Journal of Remote Sensing, 2005, 26(9): 1959-1977.
     [8] Chen, J., Chen, J., Liao, A. P., et al. Global land cover mapping at 30 m resolution: A POK-based operational approach [J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 103: 7-27.
     [9] Zhang, X., Liu, L. Y., Wu, C. S., et al. Development of a global 30 m impervious surface map using multisource and multitemporal remote sensing datasets with the Google Earth Engine platform [J]. Earth System Science Data, 2020, 12: 1625-1648.
     [10] Pesaresi, M., Guo, H. D., Blaes, X., et al. A global human settlement layer from optical HR/VHR RS data: concept and first results [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 6(5): 2102-2131.
     [11] Esch, T., Heldens, W., Hirner, A., et al. Breaking new ground in mapping human settlements from space: the global urban footprint [J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 134: 30-42.
     [12] Sun, J., Sun, Z. C., Guo, H. D., et al. A dataset of built-up areas of Chinese cities in 2020[J]. China Scientific Data, 2022, 7(1): 190-204.
     [13] Xu, Z. N., Gao, X. L. A novel method for identifying the boundary of urban built-up areas with POI data [J]. Acta Geographica Sinica, 2016, 71(6): 928-939.
     [14] Zheng, H. H., Gui, Z. P., Li, F., et al. An extraction method for urban built-up areas by integrating nighttime light data and point-of-interest data [J]. Geography and Geo-Information Science, 2019, 35(2): 25-32.
     [15] Zhang, J. Q., Shi, W. B., Xiu, C. L. Applications of POI data in urban studies in China [J]. Scientia Geographica Sinica, 2021, 41(1): 140-148.
     [16] Zhang, Y., Zheng, F. J., Liu, Y. F., et al. Extraction of urban built-up areas based on POI and impervious surface index [J]. Scientia Geographica Sinica, 2022, 42(3): 506-514.

Data Product:

ID Data Name Data Size Operation
1 ChinaUrbanArea_2015_2020_2025.rar 2699.27KB
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