{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T01:13:33Z","timestamp":1776215613459,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2019,11,19]],"date-time":"2019-11-19T00:00:00Z","timestamp":1574121600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key R&amp;D Program of China","award":["2017YFA0604403"],"award-info":[{"award-number":["2017YFA0604403"]}]},{"name":"the Natural Science Foundation of the Guangdong Province of China","award":["2016A030313230"],"award-info":[{"award-number":["2016A030313230"]}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41601445"],"award-info":[{"award-number":["41601445"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Young Scholar Research Fund of Sun Yat-sen University","award":["16lgpy05"],"award-info":[{"award-number":["16lgpy05"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Land use and land cover (LULC) are diverse and complex in urban areas. Remotely sensed images are commonly used for land cover classification but hardly identifies urban land use and functional areas because of the semantic gap (i.e., different definitions of similar or identical buildings). Social media data, \u201cmarks\u201d left by people using mobile phones, have great potential to overcome this semantic gap. Multisource remote sensing data are also expected to be useful in distinguishing different LULC types. This study examined the capability of combined multisource remote sensing images and social media data in urban LULC classification. Multisource remote sensing images included a Chinese ZiYuan-3 (ZY-3) high-resolution image, a Landsat 8 Operational Land Imager (OLI) multispectral image, and a Sentinel-1A synthetic aperture radar (SAR) image. Social media data consisted of the hourly spatial distribution of WeChat users, which is a ubiquitous messaging and payment platform in China. LULC was classified into 10 types, namely, vegetation, bare land, road, water, urban village, greenhouses, residential, commercial, industrial, and educational buildings. A method that integrates object-based image analysis, decision trees, and random forests was used for LULC classification. The overall accuracy and kappa value attained by the combination of multisource remote sensing images and WeChat data were 87.55% and 0.84, respectively. They further improved to 91.55% and 0.89, respectively, by integrating the textural and spatial features extracted from the ZY-3 image. The ZY-3 high-resolution image was essential for urban LULC classification because it is necessary for the accurate delineation of land parcels. The addition of Landsat 8 OLI, Sentinel-1A SAR, or WeChat data also made an irreplaceable contribution to the classification of different LULC types. The Landsat 8 OLI image helped distinguish between the urban village, residential buildings, commercial buildings, and roads, while the Sentinel-1A SAR data reduced the confusion between commercial buildings, greenhouses, and water. Rendering the spatial and temporal dynamics of population density, the WeChat data improved the classification accuracies of an urban village, greenhouses, and commercial buildings.<\/jats:p>","DOI":"10.3390\/rs11222719","type":"journal-article","created":{"date-parts":[[2019,11,19]],"date-time":"2019-11-19T11:30:17Z","timestamp":1574163017000},"page":"2719","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":70,"title":["Urban Land Use and Land Cover Classification Using Multisource Remote Sensing Images and Social Media Data"],"prefix":"10.3390","volume":"11","author":[{"given":"Yan","family":"Shi","sequence":"first","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China"}]},{"given":"Zhixin","family":"Qi","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China"}]},{"given":"Xiaoping","family":"Liu","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China"}]},{"given":"Ning","family":"Niu","sequence":"additional","affiliation":[{"name":"School of Resources and Environment, Academician Laboratory for Urban and Rural Spatial Data Mining of Henan Province, Henan University of Economics and Law, Zhengzhou 450000, China"}]},{"given":"Hui","family":"Zhang","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,19]]},"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","first-page":"1323","DOI":"10.3390\/s7071323","article-title":"Land use and land cover change in Guangzhou, China, from 1998 to 2003, based on Landsat TM\/ETM+ imagery","volume":"7","author":"Fan","year":"2007","journal-title":"Sensors"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"4227","DOI":"10.1080\/01431160600702426","article-title":"Developing land use\/land cover parameterization for climate\u2013land modelling in East Africa","volume":"27","author":"Torbick","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_4","first-page":"275","article-title":"Managing land use\/cover data harmonized to support land administration and environmental applications in Turkey","volume":"5","author":"Aydinoglu","year":"2010","journal-title":"Sci. Res. Essays"},{"key":"ref_5","first-page":"96","article-title":"Investigating relationships between Landsat-7 ETM+ data and spatial segregation of LULC types under shifting agriculture in southern Cameroon","volume":"8","author":"Yemefack","year":"2006","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1055","DOI":"10.3390\/rs1041055","article-title":"Investigating the Impacts of Landuse-landcover (LULC) Change in the Pearl River Delta Region on Water Quality in the Pearl River Estuary and Hong Kong\u2019s Coast","volume":"1","author":"Zhang","year":"2009","journal-title":"Remote Sens."},{"key":"ref_7","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_8","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.landurbplan.2017.09.019","article-title":"A future land use simulation model (FLUS) for simulating multiple land use scenarios by coupling human and natural effects","volume":"168","author":"Liu","year":"2017","journal-title":"Landsc. Urban Plan."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/S0959-3780(01)00007-3","article-title":"The causes of land-use and land-cover change: Moving beyond the myths","volume":"11","author":"Lambin","year":"2001","journal-title":"Glob. Environ. Chang. Hum. Policy Dimens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.rse.2006.02.010","article-title":"Use of impervious surface in urban land-use classification","volume":"102","author":"Lu","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_11","unstructured":"Wegmuller, U., Strozzi, T., and Bitelli, G. (July, January 28). Validation of ERS differential SAR interferometry for land subsidence mapping: The Bologna case study. Proceedings of the IEEE 1999 International Geoscience and Remote Sensing Symposium, IGARSS\u201999, Hamburg, Germany."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.rse.2011.11.001","article-title":"A novel algorithm for land use and land cover classification using RADARSAT-2 polarimetric SAR data","volume":"118","author":"Qi","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Luo, H., Lin, L., Zhu, H., Kuai, X., Zhang, Z., and Liu, Y. (2016). Land Cover Extraction from High Resolution ZY-3 Satellite Imagery Using Ontology-Based Method. ISPRS Int. J. Geo-Inf., 5.","DOI":"10.3390\/ijgi5030031"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1080\/19479830903562041","article-title":"Fusing high-resolution SAR and optical imagery for improved urban land cover study and classification","volume":"1","author":"Amarsaikhan","year":"2010","journal-title":"Int. J. Image Data Fusion"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"768","DOI":"10.1109\/36.298006","article-title":"Multisource classification of remotely sensed data: Fusion of Landsat TM and SAR images","volume":"32","author":"Solberg","year":"1994","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1391","DOI":"10.1080\/01431160903475415","article-title":"Fusion of Quickbird MS and RADARSAT SAR data for urban land-cover mapping: Object-based and knowledge-based approach","volume":"31","author":"Ban","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Liu, C., and Song, G. (2011, January 21\u201323). A method of measuring the semantic gap in image retrieval: Using the information theory. Proceedings of the International Conference on Image Analysis & Signal Processing, Hubei, China.","DOI":"10.1109\/IASP.2011.6109048"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Bahmanyar, R. (2013, January 15\u201318). Measuring the semantic gap using a Communication Channel model. Proceedings of the IEEE International Conference on Image Processing, Melbourne, Australia.","DOI":"10.1109\/ICIP.2013.6738902"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/J.ENG.2016.02.003","article-title":"Urban big data and the development of city intelligence","volume":"2","author":"Pan","year":"2016","journal-title":"Engineering"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Hu, T., Yang, J., Li, X., 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_21","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_22","first-page":"21","article-title":"Exploring place through user-generated content: Using Flickr to describe city cores","volume":"1","author":"Purves","year":"2010","journal-title":"J. Spat. Inf. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Birkin, M., Harland, K., and Malleson, N. (2013, January 24\u201327). The Classification of Space-Time Behaviour Patterns in a British City from Crowd-Sourced Data. Proceedings of the International Conference on Computational Science and Its Applications, Ho Chi Minh City, Vietnam.","DOI":"10.1007\/978-3-642-39649-6_13"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wakamiya, S., Lee, R., and Sumiya, K. (2011, January 12\u201313). Urban Area Characterization Based on Semantics of Crowd Activities in Twitter. Proceedings of the International Conference on Geospatial Semantics, Brest, France.","DOI":"10.1007\/978-3-642-20630-6_7"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.envsoft.2017.02.004","article-title":"Automatic land cover classification of geo-tagged field photos by deep learning","volume":"91","author":"Xu","year":"2017","journal-title":"Environ. Model. Softw."},{"key":"ref_26","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_27","unstructured":"(2018, February 28). Guangzhou Statistics Bureau, Available online: http:\/\/www.gzstats.gov.cn\/."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Amitrano, D., Cecinati, F., Martino, G.D., Iodice, A., Riccio, D., and Ruello, G. (2015, January 26\u201331). Sentinel-1 Multitemporal SAR Products. Proceedings of the Geoscience & Remote Sensing Symposium, Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326695"},{"key":"ref_29","first-page":"2423","article-title":"Relationship between built environment of rational pedestrian catchment areas and URT commuting ridership: Evidence from 44 URT stations in Beijing","volume":"73","author":"Shen","year":"2018","journal-title":"J. Geogr. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1111\/j.1477-9730.2005.00317_4.x","article-title":"Field Methods in Remote Sensing","volume":"20","author":"Wallington","year":"2005","journal-title":"Photogramm. Rec."},{"key":"ref_31","first-page":"1187","article-title":"Synthetic Aperture Radar (Sar) Based Classifiers for Land Applications in Germany","volume":"41","author":"Suresh","year":"2016","journal-title":"Int. Arch. Photogramm."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Yommy, A.S., Liu, R., and Wu, S. (2015, January 26\u201327). SAR Image Despeckling Using Refined Lee Filter. Proceedings of the International Conference on Intelligent Human-machine Systems & Cybernetics, Hangzhou, China.","DOI":"10.1109\/IHMSC.2015.236"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1007\/s12524-008-0020-4","article-title":"Multi-resolution Segmentation for Object-based Classification and Accuracy Assessment of Land Use\/Land Cover Classification using Remotely Sensed Data","volume":"36","author":"Rahman","year":"2008","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhou, Y.N., Feng, L., Chen, Y.H., and Li, J. (2017, January 23\u201328). Object-Based Land Cover Mapping using Adaptive Scale Segmentation from ZY-3 Satellite images. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8126894"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1175","DOI":"10.1080\/01431161.2017.1395968","article-title":"A Random Forests classification method for urban land-use mapping integrating spatial metrics and texture analysis","volume":"39","author":"Hernandez","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"4287","DOI":"10.1080\/0143116042000192367","article-title":"A multiscale texture analysis procedure for improved forest stand classification","volume":"25","author":"Coburn","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.isprsjprs.2007.08.007","article-title":"Object-based classification using Quickbird imagery for delineating forest vegetation polygons in a Mediterranean test site","volume":"63","author":"Mallinis","year":"2008","journal-title":"ISPRS J. Photogramm."},{"key":"ref_38","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_39","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1080\/01431160412331269698","article-title":"Random forest classifier for remote sensing classification","volume":"26","author":"Pal","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.rse.2011.12.003","article-title":"Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture","volume":"121","author":"Atkinson","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"3440","DOI":"10.1080\/01431161.2014.903435","article-title":"Land-use\/cover classification in a heterogeneous coastal landscape using RapidEye imagery: Evaluating the performance of random forest and support vector machines classifiers","volume":"35","author":"Adam","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Vassileva, M., Nascetti, A., Giuliotonolo, F., and Boccardo, P. (2015, January 26\u201331). Unsupervised flood extent detection from SAR imagery applying shadow filtering from SAR simulated image. Proceedings of the Geoscience & Remote Sensing Symposium, Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326372"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/22\/2719\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:35:50Z","timestamp":1760189750000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/22\/2719"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,19]]},"references-count":42,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2019,11]]}},"alternative-id":["rs11222719"],"URL":"https:\/\/doi.org\/10.3390\/rs11222719","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11,19]]}}}