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Data Details

Experimental Result Dataset of Remote Sensing-Based Extraction of Urban and Rural Construction Land in Xining City, China (2005, 2024)


LIU Huimin1,2LIU Zhifeng*3,2LIN Zibin1,2WEN Jingping1,2LI Hehui1,2LIU Shuhui3,4MAO Xufeng1,2WANG Yihang5HE Chunyang3,2
1 Key Laboratory of Tibetan Plateau Land Surface Processes and Ecological Conservation (Ministry of Education)，Qinghai Normal University, Qinghai Provincial Key Laboratory of Physical Geography and Environmental Processes，Xining 810008，China2 College of Geographical Sciences，Qinghai Normal University, Academy of Plateau Science and Sustainability，People's Government of Qinghai Province and Beijing Normal University，Xining 810008，China3 State Key Laboratory of Earth Surface Processes and Disaster Risk Reduction，Beijing Normal University，Beijing 100875，China4 Qinghai Provincial Institute of Territorial Spatial Planning，Xining 810008，China5 Wangjing School Affiliated to Tsinghua University High School，Beijing 100102，China

DOI:10.3974/geodb.2026.08.05.V1

Published:Aug. 2026

Visitors：52       Data Files Downloaded：0      
Data Downloaded： 无      Citations：

Key Words：

spatial distribution,high-resolution remote sensing,visual interpretation,urban and rural construction land,Xining,experiment

Abstract：

The Experimental Result Dataset of Remote Sensing-Based Extracting of Urban and Rural Construction Land in Xining City, China (2005, 2024) was developed using high-resolution remote sensing images including QuickBird-2 in 2005, and Pleiades-1A/1B, GeoEye-1, and the WorldView series in 2024. A method integrating manual visual interpretation and vectorization was employed to extract spatial information on three categories of construction land: urban, town, and rural. The dataset includes the construction land at both citywide and county levels in Xining for 2005 and 2024. The data was standardized to the China Geodetic Coordinate System 2000 (CGCS2000; EPSG: 4490). The dataset is archived in .shp data format, and consists of 82 data files with data size of 77.4 MB (compressed into 1 file with 21.5 MB).

Foundation Item：

Data Citation：

LIU Huimin, LIU Zhifeng*, LIN Zibin, WEN Jingping, LI Hehui, LIU Shuhui, MAO Xufeng, WANG Yihang, HE Chunyang. Experimental Result Dataset of Remote Sensing-Based Extraction of Urban and Rural Construction Land in Xining City, China (2005, 2024)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2026. https://doi.org/10.3974/geodb.2026.08.05.V1.
.

References：


     [1] Liu, J. Y., Ning, J., Kuang, W. H., et al. Spatiotemporal patterns and new characteristics of land use change in China from 2010 to 2015 [J]. Acta Geographica Sinica, 2018, 73(5): 789-802.
     [2] Wang, L., Li, C. C., Ying, Q., et al. Satellite remote sensing mapping of urban expansion in China from 1990 to 2010 [J]. Chinese Science Bulletin, 2012, 57(16): 1388-1403.
     [3] Gong, P., Li, X. C., Wang, J., et al. Annual maps of global artificial impervious area (GAIA) between 1985 and 2018 [J]. Remote Sensing of Environment, 2020, 236: 111510.
     [4] Chen, J., Chen, J., Liao, A. P., et al. Global land cover mapping at 30m resolution: A POK-based operational approach [J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 103: 7-27.
     [5] Gong, P., Liu, H., Zhang, M. N., et al. Stable classification with limited sample: transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017 [J]. Science Bulletin, 2019, 64(6): 370-373.
     [6] Huang, X., Li, J. Y., Yang, J., et al. 30 m global impervious surface area dynamics and urban expansion pattern observed by Landsat satellites: From 1972 to 2019 [J]. Science China Earth Sciences, 2021, 64(11): 1922-1933.
     [7] Yang, J., Huang, X. The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019 [J]. Earth System Science Data, 2021, 13(8): 3907-3925.
     [8] Zhang, X., Liu, L., Chen, X., et al. GLC_FCS30: global land-cover product with fine classification system at 30 m using time-series Landsat imagery [J]. Earth System Science Data, 2021, 13(6): 2753-2776.
     [9] Liu, Z. F., Ying, J. H., He, C. Y., et al. Global urban expansion simulation dataset (1992-2050) [J]. Journal of Global Change Data & Discovery, 2024, 8(1): 90-97.
     [10] Li, Z., He, W., Cheng, M., et al. SinoLC-1: the first 1 m resolution national-scale land-cover map of China created with a deep learning framework and open-access data [J]. Earth System Science Data, 2023, 15(11): 4749-4780.
     [11] Brown, C. F., Brumby, S. P., Guzder-Williams, B., et al. Dynamic World, Near real-time global 10 m land use land cover mapping [J]. Scientific Data, 2022, 9: 251.
     [12] Gao, J., O’Neill, B. C. Mapping global urban land for the 21st century with data-driven simulations and Shared Socioeconomic Pathways [J]. Nature Communications, 2020, 11(1): 2302.
     [13] Liu, X. P., Hu, G. H., Chen, Y. M., et al. High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform [J]. Remote Sensing of Environment, 2018, 209: 227-239.
     [14] Zhao, Y. Y., Gong, P., Yu, L., et al. Towards a common validation sample set for global land-cover mapping [J]. International Journal of Remote Sensing, 2014, 35(13): 4795-4814.
     [15] Gong, P., Wang, J., Yu, L., et al. Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data [J]. International Journal of Remote Sensing, 2013, 34(7): 2607-2654.

Data Product:

ID Data Name Data Size Operation
1 XiningC&RConstructionLand.rar 22117.01KB
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