{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T21:40:41Z","timestamp":1773438041246,"version":"3.50.1"},"reference-count":53,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,6,19]],"date-time":"2023-06-19T00:00:00Z","timestamp":1687132800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFB3904205"],"award-info":[{"award-number":["2022YFB3904205"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Physical urban boundaries (PUBs) are basic geographic information data for defining the spatial extent of urban landscapes with non-agricultural land and non-agricultural economic activities. Accurately mapping PUBs provides a spatiotemporal database for urban dynamic monitoring, territorial spatial planning, and ecological environment protection. However, traditional extraction methods often have problems, such as subjective parameter settings and inconsistent cartographic scales, making it difficult to identify PUBs objectively and accurately. To address these problems, we proposed a self-supervised learning approach for PUB extraction. First, we used nighttime light and OpenStreetMap road data to map the initial urban boundary for data preparation. Then, we designed a pretext task of self-supervised learning based on an unsupervised mutation detection algorithm to automatically mine supervised information in unlabeled data, which can avoid subjective human interference. Finally, a downstream task was designed as a supervised learning task in Google Earth Engine to classify urban and non-urban areas using impervious surface density and nighttime light data, which can solve the scale inconsistency problem. Based on the proposed method, we produced a 30 m resolution China PUB dataset containing six years (i.e., 1995, 2000, 2005, 2010, 2015, and 2020). Our PUBs show good agreement with existing products and accurately describe the spatial extent of urban areas, effectively distinguishing urban and non-urban areas. Moreover, we found that the gap between the national per capita GDP and the urban per capita GDP is gradually decreasing, but regional coordinated development and intensive development still need to be strengthened.<\/jats:p>","DOI":"10.3390\/rs15123189","type":"journal-article","created":{"date-parts":[[2023,6,20]],"date-time":"2023-06-20T01:59:30Z","timestamp":1687226370000},"page":"3189","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A Self-Supervised Learning Approach for Extracting China Physical Urban Boundaries Based on Multi-Source Data"],"prefix":"10.3390","volume":"15","author":[{"given":"Yuan","family":"Tao","sequence":"first","affiliation":[{"name":"School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China"},{"name":"National Geomatics Center of China, Beijing 100830, China"},{"name":"Key Laboratory of Spatio-Temporal Information and Intelligent Services, Ministry of Natural Resources of China, Beijing 100830, China"}]},{"given":"Wanzeng","family":"Liu","sequence":"additional","affiliation":[{"name":"National Geomatics Center of China, Beijing 100830, China"},{"name":"Key Laboratory of Spatio-Temporal Information and Intelligent Services, Ministry of Natural Resources of China, Beijing 100830, China"}]},{"given":"Jun","family":"Chen","sequence":"additional","affiliation":[{"name":"National Geomatics Center of China, Beijing 100830, China"},{"name":"Key Laboratory of Spatio-Temporal Information and Intelligent Services, Ministry of Natural Resources of China, Beijing 100830, China"}]},{"given":"Jingxiang","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China"}]},{"given":"Ran","family":"Li","sequence":"additional","affiliation":[{"name":"National Geomatics Center of China, Beijing 100830, China"},{"name":"Key Laboratory of Spatio-Temporal Information and Intelligent Services, Ministry of Natural Resources of China, Beijing 100830, China"}]},{"given":"Jiaxin","family":"Ren","sequence":"additional","affiliation":[{"name":"National Geomatics Center of China, Beijing 100830, China"},{"name":"Key Laboratory of Spatio-Temporal Information and Intelligent Services, Ministry of Natural Resources of China, Beijing 100830, China"},{"name":"School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"}]},{"given":"Xiuli","family":"Zhu","sequence":"additional","affiliation":[{"name":"National Geomatics Center of China, Beijing 100830, China"},{"name":"Key Laboratory of Spatio-Temporal Information and Intelligent Services, Ministry of Natural Resources of China, Beijing 100830, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.habitatint.2017.11.009","article-title":"Assessment on the urbanization strategy in China: Achievements, challenges and reflections","volume":"71","author":"Guan","year":"2018","journal-title":"Habitat Int."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Chen, M., Zhang, H., Liu, W., and Zhang, W. (2014). The global pattern of urbanization and economic growth: Evidence from the last three decades. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0103799"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1038\/s41586-018-0411-9","article-title":"Global land change from 1982 to 2016","volume":"560","author":"Song","year":"2018","journal-title":"Nature"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Li, Y., Li, X., and Lu, T. (2023). Coupled Coordination Analysis between Urbanization and Eco-Environment in Ecologically Fragile Areas: A Case Study of Northwestern Sichuan, Southwest China. Remote Sens., 15.","DOI":"10.3390\/rs15061661"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1038\/nature13945","article-title":"Implications of agricultural transitions and urbanization for ecosystem services","volume":"515","author":"Cumming","year":"2014","journal-title":"Nature"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1038\/s41893-020-0521-x","article-title":"High-spatiotemporal-resolution mapping of global urban change from 1985 to 2015","volume":"3","author":"Liu","year":"2020","journal-title":"Nat. Sustain."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Seto, K.C., Fragkias, M., Guneralp, B., and Reilly, M.K. (2011). A meta-analysis of global urban land expansion. PLoS ONE, 6.","DOI":"10.1371\/journal.pone.0023777"},{"key":"ref_8","first-page":"289","article-title":"Toward establishing the concept of physical urban area in China","volume":"50","author":"Zhou","year":"1995","journal-title":"Acta Geogr. Sin."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"104061","DOI":"10.1016\/j.cities.2022.104061","article-title":"Extracting physical urban areas of 81 major Chinese cities from high-resolution land uses","volume":"131","author":"Zhang","year":"2022","journal-title":"Cities"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.cities.2016.08.014","article-title":"Delineation of an urban agglomeration boundary based on Sina Weibo microblog \u2018check-in\u2019 data: A case study of the Yangtze River Delta","volume":"60","author":"Zhen","year":"2017","journal-title":"Cities"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1293","DOI":"10.1080\/13658816.2017.1282615","article-title":"Depicting urban boundaries from a mobility network of spatial interactions: A case study of Great Britain with geo-located Twitter data","volume":"31","author":"Yin","year":"2017","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s41651-020-00047-6","article-title":"Defining the Boundaries of Urban Built-up Area Based on Taxi Trajectories: A Case Study of Beijing","volume":"4","author":"Li","year":"2020","journal-title":"J. Geovis. Spat. Anal."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.compenvurbsys.2013.07.003","article-title":"Defining and characterizing urban boundaries: A fractal analysis of theoretical cities and Belgian cities","volume":"41","author":"Tannier","year":"2013","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1111\/j.1538-4632.2011.00814.x","article-title":"A Fractal Approach to Identifying Urban Boundaries","volume":"43","author":"Tannier","year":"2011","journal-title":"Geogr. Anal."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Li, X., Zheng, K., Qin, F., Wang, H., and Zhao, C. (2022). Deriving Urban Boundaries of Henan Province, China, Based on Sentinel-2 and Deep Learning Methods. Remote Sens., 14.","DOI":"10.3390\/rs14153752"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Dai, X., Jin, J., Chen, Q., and Fang, X. (2022). On Physical Urban Boundaries, Urban Sprawl, and Compactness Measurement: A Case Study of the Wen-Tai Region, China. Land, 11.","DOI":"10.3390\/land11101637"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.habitatint.2015.01.017","article-title":"Urban boundary extraction and sprawl analysis using Landsat images: A case study in Wuhan, China","volume":"47","author":"Hu","year":"2015","journal-title":"Habitat Int."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"18702","DOI":"10.1073\/pnas.0807435105","article-title":"Laws of population growth","volume":"105","author":"Rozenfeld","year":"2008","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1269","DOI":"10.1080\/13658816.2010.510801","article-title":"Zipf\u2019s law for all the natural cities in the United States: A geospatial perspective","volume":"25","author":"Jiang","year":"2011","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"180468","DOI":"10.1098\/rsos.180468","article-title":"A worldwide model for boundaries of urban settlements","volume":"5","author":"Oliveira","year":"2018","journal-title":"Roy. Soc. Open Sci."},{"key":"ref_21","first-page":"103041","article-title":"Identifying and evaluating suburbs in China from 2012 to 2020 based on SNPP\u2013VIIRS nighttime light remotely sensed data","volume":"114","author":"Liu","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"094044","DOI":"10.1088\/1748-9326\/ab9be3","article-title":"Mapping global urban boundaries from the global artificial impervious area (GAIA) data","volume":"15","author":"Li","year":"2020","journal-title":"Environ. Res. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.landurbplan.2018.03.008","article-title":"A new approach for urban-rural fringe identification: Integrating impervious surface area and spatial continuous wavelet transform","volume":"175","author":"Peng","year":"2018","journal-title":"Landsc. Urban Plan."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1080\/13658816.2021.1876236","article-title":"A constraint-based approach for identifying the urban\u2013rural fringe of polycentric cities using multi-sourced data","volume":"36","author":"Yang","year":"2022","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"111353","DOI":"10.1016\/j.rse.2019.111353","article-title":"A new ranking of the world\u2019s largest cities\u2014Do administrative units obscure morphological realities?","volume":"232","author":"Weigand","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Schiappa, M.C., Rawat, Y.S., and Shah, M. (2022). Self-supervised learning for videos: A survey. ACM Comput. Surv.","DOI":"10.1145\/3577925"},{"key":"ref_27","first-page":"1","article-title":"Global and local contrastive self-supervised learning for semantic segmentation of HR remote sensing images","volume":"60","author":"Li","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","unstructured":"Zbontar, J., Jing, L., Misra, I., LeCun, Y., and Deny, S. (2021, January 18\u201324). Barlow twins: Self-supervised learning via redundancy reduction. Proceedings of the International Conference on Machine Learning, PMLR, Virtual."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhao, Z., Luo, Z., Li, J., Chen, C., and Piao, Y. (2020). When Self-Supervised Learning Meets Scene Classification: Remote Sensing Scene Classification Based on a Multitask Learning Framework. Remote Sens., 12.","DOI":"10.3390\/rs12203276"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Stojnic, V., and Risojevic, V. (2021, January 19\u201325). Self-supervised learning of remote sensing scene representations using contrastive multiview coding. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPRW53098.2021.00129"},{"key":"ref_31","first-page":"103130","article-title":"Self-supervised audiovisual representation learning for remote sensing data","volume":"116","author":"Heidler","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1109\/MPRV.2008.80","article-title":"Openstreetmap: User-generated street maps","volume":"7","author":"Haklay","year":"2008","journal-title":"IEEE Pervas. Comput."},{"key":"ref_33","unstructured":"National Geomatics Center of China (2022, March 29). 1: 1 Million Public Version of Basic Geographic Information Data. Available online: https:\/\/www.webmap.cn\/commres.do?method=result100W."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Liu, X., Ning, X., Wang, H., Wang, C., Zhang, H., and Meng, J. (2019). A Rapid and Automated Urban Boundary Extraction Method Based on Nighttime Light Data in China. Remote Sens., 11.","DOI":"10.3390\/rs11091126"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2587","DOI":"10.1109\/JSTARS.2018.2844566","article-title":"Urban land extraction using DMSP\/OLS nighttime light data and OpenStreetMap datasets for cities in China at different development levels","volume":"11","author":"Cheng","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/j.rse.2018.10.015","article-title":"A global record of annual urban dynamics (1992\u20132013) from nighttime lights","volume":"219","author":"Zhou","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wu, H., Wang, L., Zhang, Z., and Gao, J. (2021). Analysis and optimization of 15-minute community life circle based on supply and demand matching: A case study of Shanghai. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0256904"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Li, F., Yan, Q., Bian, Z., Liu, B., and Wu, Z. (2020). A POI and LST adjusted NTL urban index for urban built-up area extraction. Sensors, 20.","DOI":"10.3390\/s20102918"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"04020034","DOI":"10.1061\/(ASCE)UP.1943-5444.0000598","article-title":"Identifying Shrinking Cities with NPP-VIIRS Nightlight Data in China","volume":"146","author":"Jiang","year":"2020","journal-title":"J. Urban Plan. Dev."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1016\/j.rse.2019.02.019","article-title":"A simple self-adjusting model for correcting the blooming effects in DMSP-OLS nighttime light images","volume":"224","author":"Cao","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1080\/02693799208901921","article-title":"Algorithms for automated line generalization1 based on a natural principle of objective generalization","volume":"6","author":"Li","year":"1992","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Masili\u016bnas, D., Tsendbazar, N.-E., Herold, M., and Verbesselt, J. (2021). BFAST Lite: A Lightweight Break Detection Method for Time Series Analysis. Remote Sens., 13.","DOI":"10.3390\/rs13163308"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.rse.2009.08.014","article-title":"Detecting trend and seasonal changes in satellite image time series","volume":"114","author":"Verbesselt","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_45","first-page":"3","article-title":"STL: A seasonal-trend decomposition","volume":"6","author":"Cleveland","year":"1990","journal-title":"J. Off. Stat."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1162\/003355399556133","article-title":"Zipf\u2019s law for cities: An explanation","volume":"114","author":"Gabaix","year":"1999","journal-title":"Q. J. Econ."},{"key":"ref_47","first-page":"102480","article-title":"Mapping hierarchical urban boundaries for global urban settlements","volume":"103","author":"Xu","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Deng, Y., and Yang, R. (2021). Influence mechanism of production-living-ecological space changes in the urbanization process of Guangdong province, China. Land, 10.","DOI":"10.3390\/land10121357"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Stevens, F.R., Gaughan, A.E., Linard, C., and Tatem, A.J. (2015). Disaggregating census data for population mapping using random forests with remotely-sensed and ancillary data. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0107042"},{"key":"ref_50","unstructured":"International Monetary Fund (2023, January 06). Government Finance Statistics. Available online: https:\/\/data.imf.org\/?sk=a0867067-d23c-4ebc-ad23-d3b015045405."},{"key":"ref_51","first-page":"1436","article-title":"Spatio-temporal pattern analysis of aritificial surface use efficiency based on Globeland30","volume":"46","author":"Li","year":"2016","journal-title":"Sci. Sin. Terrae"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Yu, S., Wang, C., Jin, Z., Zhang, S., and Miao, Y. (2022). Spatiotemporal evolution and driving mechanism of regional shrinkage at the county scale: The three provinces in northeastern China. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0271909"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"102811","DOI":"10.1016\/j.scs.2021.102811","article-title":"Understanding patterns and multilevel influencing factors of small town shrinkage in Northeast China","volume":"68","author":"Tong","year":"2021","journal-title":"Sustain. Cities Soc."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/12\/3189\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:56:42Z","timestamp":1760126202000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/12\/3189"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,19]]},"references-count":53,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["rs15123189"],"URL":"https:\/\/doi.org\/10.3390\/rs15123189","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,19]]}}}