{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T00:26:17Z","timestamp":1772583977032,"version":"3.50.1"},"reference-count":80,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2019,6,21]],"date-time":"2019-06-21T00:00:00Z","timestamp":1561075200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Plan","award":["2017YFB0504205"],"award-info":[{"award-number":["2017YFB0504205"]}]},{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["41622109"],"award-info":[{"award-number":["41622109"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["41371017"],"award-info":[{"award-number":["41371017"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Urban areas are essential to daily human life; however, the urbanization process also brings about problems, especially in China. Urban mapping at large scales relies heavily on remote sensing (RS) data, which cannot capture socioeconomic features well. Geolocation datasets contain patterns of human movement, which are closely related to the extent of urbanization. However, the integration of RS and geolocation data for urban mapping is performed mostly at the city level or finer scales due to the limitations of geolocation datasets. Tencent provides a large-scale location request density (LRD) dataset with a finer temporal resolution, and makes large-scale urban mapping possible. The objective of this study is to combine multi-source features from RS and geolocation datasets to extract information on urban areas at large scales, including night-time lights, vegetation cover, land surface temperature, population density, LRD, accessibility, and road networks. The random forest (RF) classifier is introduced to deal with these high-dimension features on a 0.01 degree grid. High spatial resolution land cover (LC) products and the normalized difference built-up index from Landsat are used to label all of the samples. The RF prediction results are evaluated using validation samples and compared with LC products for four typical cities. The results show that night-time lights and LRD features contributed the most to the urban prediction results. A total of 176,266 km2 of urban areas in China were extracted using the RF classifier, with an overall accuracy of 90.79% and a kappa coefficient of 0.790. Compared with existing LC products, our results are more consistent with the manually interpreted urban boundaries in the four selected cities. Our results reveal the potential of Tencent LRD data for the extraction of large-scale urban areas, and the reliability of the RF classifier based on a combination of RS and geolocation data.<\/jats:p>","DOI":"10.3390\/rs11121470","type":"journal-article","created":{"date-parts":[[2019,6,21]],"date-time":"2019-06-21T11:54:31Z","timestamp":1561118071000},"page":"1470","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Mapping Urban Areas Using a Combination of Remote Sensing and Geolocation Data"],"prefix":"10.3390","volume":"11","author":[{"given":"Nan","family":"Xia","sequence":"first","affiliation":[{"name":"Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210093, China"},{"name":"Department of Geographic Information Science, Nanjing University, Nanjing 210093, China"},{"name":"Fenner School of Environment and Society, Australian National University, Canberra, ACT 2601, Australia"}]},{"given":"Liang","family":"Cheng","sequence":"additional","affiliation":[{"name":"Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210093, China"},{"name":"Department of Geographic Information Science, Nanjing University, Nanjing 210093, China"},{"name":"Collaborative Innovation Center for the South Sea Studies, Nanjing University, Nanjing 210093, China"}]},{"given":"ManChun","family":"Li","sequence":"additional","affiliation":[{"name":"Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210093, China"},{"name":"Department of Geographic Information Science, Nanjing University, Nanjing 210093, China"},{"name":"Collaborative Innovation Center for the South Sea Studies, Nanjing University, Nanjing 210093, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,6,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1126\/science.1111772","article-title":"Global consequences of land use","volume":"309","author":"Foley","year":"2005","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Schneider, A., Friedl, M.A., and Potere, D. (2009). A new map of global urban extent from MODIS satellite data. Environ. Res. Lett., 4.","DOI":"10.1088\/1748-9326\/4\/4\/044003"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2016.10.002","article-title":"Updating urban extents with nighttime light imagery by using an object-based thresholding method","volume":"187","author":"Xie","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1016\/j.rse.2016.02.010","article-title":"Remotely sensed assessment of urbanization effects on vegetation phenology in China\u2019s 32 major cities","volume":"176","author":"Zhou","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_5","unstructured":"National Bureau of Statistics of China (2019, January 08). Annual Statistical Yearbook of China, Available online: http:\/\/www.stats.gov.cn\/english\/Statisticaldata\/AnnualData\/."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.landusepol.2015.01.007","article-title":"Impact of urbanization on cultivated land changes in China","volume":"45","author":"Deng","year":"2015","journal-title":"Land Use Policy"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.landurbplan.2015.10.001","article-title":"The rapid and massive urban and industrial land expansions in China between 1990 and 2010: A CLUD-based analysis of their trajectories, patterns, and drivers","volume":"145","author":"Kuang","year":"2016","journal-title":"Landsc. Urban Plan."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.landurbplan.2017.02.019","article-title":"Urban land expansion and regional inequality in transitional China","volume":"163","author":"Wei","year":"2017","journal-title":"Landsc. Urban Plan."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.rse.2012.04.018","article-title":"Quantitative estimation of urbanization dynamics using time series of DMSP\/OLS nighttime light data: A comparative case study from China\u2019s cities","volume":"124","author":"Ma","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Liu, X., de Sherbinin, A., and Zhan, Y. (2019). Mapping Urban Extent at Large Spatial Scales Using Machine Learning Methods with VIIRS Nighttime Light and MODIS Daytime NDVI Data. Remote Sens., 11.","DOI":"10.3390\/rs11101247"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.rse.2014.03.004","article-title":"A cluster-based method to map urban area from DMSP\/OLS nightlights","volume":"147","author":"Zhou","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"12419","DOI":"10.3390\/rs70912419","article-title":"Mapping Urban Areas with Integration of DMSP\/OLS Nighttime Light and MODIS Data Using Machine Learning Techniques","volume":"7","author":"Jing","year":"2015","journal-title":"Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/j.rse.2017.11.026","article-title":"Using Landsat and nighttime lights for supervised pixel-based image classification of urban land cover","volume":"205","author":"Goldblatt","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.rse.2012.10.022","article-title":"The Vegetation Adjusted NTL Urban Index: A new approach to reduce saturation and increase variation in nighttime luminosity","volume":"129","author":"Zhang","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.isprsjprs.2017.11.016","article-title":"A temperature and vegetation adjusted NTL urban index for urban area mapping and analysis","volume":"135","author":"Zhang","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"7671","DOI":"10.3390\/rs70607671","article-title":"Regional Urban Extent Extraction Using Multi-Sensor Data and One-Class Classification","volume":"7","author":"Zhang","year":"2015","journal-title":"Remote. Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.rse.2015.12.042","article-title":"Mapping sub-pixel urban expansion in China using MODIS and DMSP\/OLS nighttime lights","volume":"175","author":"Huang","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1080\/00045608.2015.1018773","article-title":"Social Sensing: A New Approach to Understanding Our Socioeconomic Environments","volume":"105","author":"Liu","year":"2015","journal-title":"Ann. Assoc. Am. Geogr."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Hu, T.Y., Yang, J., Li, X.C., and Gong, P. (2016). Mapping Urban Land Use by Using Landsat Images and Open Social Data. Remote Sens., 8.","DOI":"10.3390\/rs8020151"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.landurbplan.2015.02.020","article-title":"Social media and the city: Rethinking urban socio-spatial inequality using user-generated geographic information","volume":"142","author":"Shelton","year":"2015","journal-title":"Landsc. Urban Plan."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.compenvurbsys.2018.07.003","article-title":"Identifying spatiotemporal urban activities through linguistic signatures","volume":"72","author":"Fu","year":"2018","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1016\/j.cities.2017.09.007","article-title":"City dynamics through Twitter: Relationships between land use and spatiotemporal demographics","volume":"72","author":"Gutierrez","year":"2018","journal-title":"Cities"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.isprsjprs.2015.03.011","article-title":"Semantic classification of urban buildings combining VHR image and GIS data: An improved random forest approach","volume":"105","author":"Du","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.isprsjprs.2017.09.007","article-title":"Hierarchical semantic cognition for urban functional zones with VHR satellite images and POI data","volume":"132","author":"Zhang","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1080\/15481603.2016.1250328","article-title":"Mining parameter information for building extraction and change detection with very high-resolution imagery and GIS data","volume":"54","author":"Guo","year":"2017","journal-title":"GISci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.isprsjprs.2013.09.010","article-title":"The use of single-date MODIS imagery for estimating large-scale urban impervious surface fraction with spectral mixture analysis and machine learning techniques","volume":"86","author":"Deng","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Chen, Y.H., Ge, Y., An, R., and Chen, Y. (2018). Super-Resolution Mapping of Impervious Surfaces from Remotely Sensed Imagery with Points-of-Interest. Remote Sens., 10.","DOI":"10.3390\/rs10020242"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Tu, W., Hu, Z.W., Li, L.F., Cao, J.Z., Jiang, J.C., Li, Q.P., and Li, Q.Q. (2018). Portraying Urban Functional Zones by Coupling Remote Sensing Imagery and Human Sensing Data. Remote Sens., 10.","DOI":"10.3390\/rs10010141"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.rse.2017.01.005","article-title":"Advances in using multitemporal night-time lights satellite imagery to detect, estimate, and monitor socioeconomic dynamics","volume":"192","author":"Bennett","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.rse.2017.03.003","article-title":"The impact of seasonal changes on observed nighttime brightness from 2014 to 2015 monthly VIIRS DNB composites","volume":"193","author":"Levin","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.rse.2017.06.039","article-title":"Using multi-source geospatial big data to identify the structure of polycentric cities","volume":"202","author":"Cai","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, Q.Z., Huang, H.P., Wu, W., Du, X., and Wang, H.Y. (2017). The Combined Use of Remote Sensing and Social Sensing Data in Fine-Grained Urban Land Use Mapping: A Case Study in Beijing, China. Remote Sens., 9.","DOI":"10.3390\/rs9090865"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.landurbplan.2018.07.012","article-title":"Measuring the use of green space with urban resource selection functions: An application using smartphone GPS locations","volume":"179","author":"Ladle","year":"2018","journal-title":"Landsc. Urban Plan."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Ma, T. (2018). Multi-Level Relationships between Satellite-Derived Nighttime Lighting Signals and Social Media-Derived Human Population Dynamics. Remote Sens., 10.","DOI":"10.3390\/rs10071128"},{"key":"ref_36","first-page":"1871","article-title":"Integrating multi-source big data to infer building functions","volume":"31","author":"Niu","year":"2017","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.landurbplan.2016.12.001","article-title":"Delineating urban functional areas with building-level social media data: A dynamic time warping (DTW) distance based k-medoids method","volume":"160","author":"Chen","year":"2017","journal-title":"Landsc. Urban Plan."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.apgeog.2017.07.014","article-title":"Difference of urban development in China from the perspective of passenger transport around Spring Festival","volume":"87","author":"Xu","year":"2017","journal-title":"Appl. Geogr."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.compenvurbsys.2018.06.005","article-title":"Integrating landscape metrics and socioeconomic features for urban functional region classification","volume":"72","author":"Xing","year":"2018","journal-title":"Comput. Environ. Urban"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.apgeog.2018.05.009","article-title":"The rich-club phenomenon of China\u2019s population flow network during the country\u2019s spring festival","volume":"96","author":"Wei","year":"2018","journal-title":"Appl. Geogr."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.landurbplan.2018.03.004","article-title":"The varying driving forces of urban expansion in China: Insights from a spatial-temporal analysis","volume":"174","author":"Li","year":"2018","journal-title":"Landsc. Urban Plan."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1038\/nature25181","article-title":"A global map of travel time to cities to assess inequalities in accessibility in 2015","volume":"553","author":"Weiss","year":"2018","journal-title":"Nature"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.patrec.2005.08.011","article-title":"Random Forests for land cover classification","volume":"27","author":"Gislason","year":"2006","journal-title":"Pattern Recogn. Lett."},{"key":"ref_44","first-page":"39","article-title":"These lit areas are undeveloped: Delimiting China\u2019s urban extents from thresholded nighttime light imagery","volume":"50","author":"Liu","year":"2016","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Zhang, P.Y., Pan, J.J., Xie, L.T., Zhou, T., Bai, H.R., and Zhu, Y.X. (2019). Spatial-Temporal Evolution and Regional Differentiation Features of Urbanization in China from 2003 to 2013. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8010031"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2205","DOI":"10.1016\/j.rse.2009.06.001","article-title":"A SVM-based method to extract urban areas from DMSP-OLS and SPOT VGT data","volume":"113","author":"Cao","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.isprsjprs.2011.11.002","article-title":"An assessment of the effectiveness of a random forest classifier for land-cover classification","volume":"67","author":"Ghimire","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Xu, T.T., Coco, G., and Gao, J. (2019). Extraction of urban built-up areas from nighttime lights using artificial neural network. Geocarto Int.","DOI":"10.1080\/10106049.2018.1559887"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1143","DOI":"10.1109\/JSTARS.2019.2900457","article-title":"Mapping Global Urban Areas From 2000 to 2012 Using Time-Series Nighttime Light Data and MODIS Products","volume":"12","author":"Chen","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_52","first-page":"187","article-title":"Combining QuickBird, LiDAR, and GIS topography indices to identify a single native tree species in a complex landscape using an object-based classification approach","volume":"50","author":"Pham","year":"2016","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.isprsjprs.2017.04.017","article-title":"Exploring diversity in ensemble classification: Applications in large area land cover mapping","volume":"129","author":"Mellor","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.isprsjprs.2014.05.003","article-title":"Coupling high-resolution satellite imagery with ALS-based canopy height model and digital elevation model in object-based boreal forest habitat type classification","volume":"94","author":"Rasanen","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"8489","DOI":"10.3390\/rs70708489","article-title":"On the Importance of Training Data Sample Selection in Random Forest Image Classification: A Case Study in Peatland Ecosystem Mapping","volume":"7","author":"Millard","year":"2015","journal-title":"Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2067","DOI":"10.1080\/01431161.2014.885152","article-title":"Assessing the impact of training sample selection on accuracy of an urban classification: A case study in Denver, Colorado","volume":"35","author":"Jin","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_57","unstructured":"Sulla-Menashe, D., and Friedl, M.A. (2018, December 01). User Guide to Collection 6 MODIS Land Cover (MCD12Q1 and MCD12C1) Product, Available online: https:\/\/lpdaac.usgs.gov\/sites\/default\/files\/public\/product_documentation\/mcd12_user_guide_v6.pdf."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2607","DOI":"10.1080\/01431161.2012.748992","article-title":"Finer resolution observation and monitoring of global land cover: First mapping results with Landsat TM and ETM+ data","volume":"34","author":"Gong","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"2102","DOI":"10.1109\/JSTARS.2013.2271445","article-title":"A Global Human Settlement Layer From Optical HR\/VHR RS Data: Concept and First Results","volume":"6","author":"Pesaresi","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.rse.2018.02.055","article-title":"High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform","volume":"209","author":"Liu","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_61","unstructured":"National Centers for Environment Information (NCEI) (2019, March 01). National Oceanic and Atmospheric Administration (NOAA), Available online: http:\/\/www.ngdc.noaa.gov\/eog\/viirs\/ download_monthly.html."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"3668","DOI":"10.1016\/j.rse.2008.05.009","article-title":"Regional mapping of human settlements in southeastern China with multisensor remotely sensed data","volume":"112","author":"Lu","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"696","DOI":"10.1021\/es2030438","article-title":"Surface Urban Heat Island Across 419 Global Big Cities","volume":"46","author":"Peng","year":"2012","journal-title":"Environ. Sci. Technol."},{"key":"ref_64","unstructured":"CIESIN\u2014Center for International Earth Science Information Network\u2014Columbia University (2019, March 20). Gridded Population of the World, Version 4 (GPWv4): Population Density, Available online: http:\/\/dx.doi.org\/10.7927\/H4NP22DQ."},{"key":"ref_65","unstructured":"(2019, May 10). Tecent Location Big Data. (In Chinese)."},{"key":"ref_66","unstructured":"Li, Y., He, P., Hu, Y., Chen, C., and Jing, N. (2015). System and Method for Processing Location Data of Target User. (Application No. 14\/699,073), U.S. Patent."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1675","DOI":"10.1080\/13658816.2017.1324976","article-title":"Classifying urban land use by integrating remote sensing and social media data","volume":"31","author":"Liu","year":"2017","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1080\/01431160304987","article-title":"Use of normalized difference built-up index in automatically mapping urban areas from TM imagery","volume":"24","author":"Zha","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"3129","DOI":"10.1016\/j.rse.2011.06.020","article-title":"A comparison of time series similarity measures for classification and change detection of ecosystem dynamics","volume":"115","author":"Lhermitte","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"9655","DOI":"10.3390\/rs70809655","article-title":"An Evaluation of Different Training Sample Allocation Schemes for Discrete and Continuous Land Cover Classification Using Decision Tree-Based Algorithms","volume":"7","author":"Colditz","year":"2015","journal-title":"Remote Sens."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1016\/j.patcog.2010.08.011","article-title":"Mining data with random forests: A survey and results of new tests","volume":"44","author":"Verikas","year":"2011","journal-title":"Pattern Recogn."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1016\/S0034-4257(03)00132-9","article-title":"An assessment of the effectiveness of decision tree methods for land cover classification","volume":"86","author":"Pal","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_73","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1080\/15481603.2017.1408892","article-title":"Less is more: Optimizing classification performance through feature selection in a very-high-resolution remote sensing object-based urban application","volume":"55","author":"Georganos","year":"2018","journal-title":"GISci. Remote Sens."},{"key":"ref_75","first-page":"49","article-title":"A framework for mapping tree species combining hyperspectral and LiDAR data: Role of selected classifiers and sensor across three spatial scales","volume":"26","author":"Ghosh","year":"2014","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.compenvurbsys.2018.04.002","article-title":"Live-Work-Play Centers of Chinese cities: Identification and temporal evolution with emerging data","volume":"71","author":"Li","year":"2018","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2018.08.018","article-title":"Tweets or nighttime lights: Comparison for preeminence in estimating socioeconomic factors","volume":"146","author":"Zhao","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Lu, H.M., Zhang, M.L., Sun, W.W., and Li, W.Y. (2018). Expansion Analysis of Yangtze River Delta Urban Agglomeration Using DMSP\/OLS Nighttime Light Imagery for 1993 to 2012. Isprs Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7020052"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"2448","DOI":"10.1016\/j.rse.2009.07.011","article-title":"A comparison of sampling designs for estimating deforestation from Landsat imagery: A case study of the Brazilian Legal Amazon","volume":"113","author":"Broich","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.apgeog.2018.03.001","article-title":"Utilizing remote sensing and big data to quantify conflict intensity: The Arab Spring as a case study","volume":"94","author":"Levin","year":"2018","journal-title":"Appl. Geogr."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/12\/1470\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:00:10Z","timestamp":1760187610000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/12\/1470"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,21]]},"references-count":80,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2019,6]]}},"alternative-id":["rs11121470"],"URL":"https:\/\/doi.org\/10.3390\/rs11121470","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,6,21]]}}}