{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T18:25:55Z","timestamp":1776363955313,"version":"3.51.2"},"reference-count":50,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2020,11,2]],"date-time":"2020-11-02T00:00:00Z","timestamp":1604275200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41871227"],"award-info":[{"award-number":["41871227"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003453","name":"Natural Science Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2020A1515010678"],"award-info":[{"award-number":["2020A1515010678"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017607","name":"Shenzhen Fundamental Research Program","doi-asserted-by":"publisher","award":["JCYJ20190808122405692"],"award-info":[{"award-number":["JCYJ20190808122405692"]}],"id":[{"id":"10.13039\/501100017607","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The ability to precisely map urban land use types can significantly aid urban planning and urban system understanding. In recent years, remote sensing images and social sensing data have been frequently used for urban land use mapping. However, there still remains a problem: what is the best basic unit for fusing remote sensing images with social sensing data? The aim of this study is to explore the impact of spatial units on urban land use mapping, with remote sensing images and social sensing data of Shenzhen City, China. Three different basic units were first applied to delineate urban land use types, and for each unit, a word dictionary was built by fusing natural\u2013physical features from high spatial resolution (HSR) remote sensing images and the socioeconomic semantic features from point of interest (POI) data. The latent Dirichlet allocation (LDA) algorithm and random forest methods were then applied to map the land use of the Futian district\u2014the core region of Shenzhen. The experiment demonstrates that: (1) No matter what kind of spatial unit, it is beneficial to fuse multisource data to improve the performance. However, when using different spatial units, the importances of features are different. (2) Using block-based spatial units results in the final map looking the best. However, a great challenge of this approach is that the scale is too coarse to handle mixed functional areas. (3) Using grid- and object-based units, the problem of mixed functional areas can be better solved. Additionally, the object-based land use map looks better from our visual interpretation. Accordingly, the results of this study could give other researchers references and advice for future studies.<\/jats:p>","DOI":"10.3390\/rs12213597","type":"journal-article","created":{"date-parts":[[2020,11,2]],"date-time":"2020-11-02T09:04:46Z","timestamp":1604307886000},"page":"3597","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Exploring Impact of Spatial Unit on Urban Land Use Mapping with Multisource Data"],"prefix":"10.3390","volume":"12","author":[{"given":"Xuanyan","family":"Dong","sequence":"first","affiliation":[{"name":"MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area and Guangdong Key Laboratory of Urban Informatics and Shenzhen Key Laboratory of Spatial Smart Sensing and Services and Research Institute for Smart Cities, Shenzhen University, Shenzhen 518060, China"},{"name":"School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yue","family":"Xu","sequence":"additional","affiliation":[{"name":"MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area and Guangdong Key Laboratory of Urban Informatics and Shenzhen Key Laboratory of Spatial Smart Sensing and Services and Research Institute for Smart Cities, Shenzhen University, Shenzhen 518060, China"},{"name":"School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Leping","family":"Huang","sequence":"additional","affiliation":[{"name":"MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area and Guangdong Key Laboratory of Urban Informatics and Shenzhen Key Laboratory of Spatial Smart Sensing and Services and Research Institute for Smart Cities, Shenzhen University, Shenzhen 518060, China"},{"name":"School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhigang","family":"Liu","sequence":"additional","affiliation":[{"name":"MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area and Guangdong Key Laboratory of Urban Informatics and Shenzhen Key Laboratory of Spatial Smart Sensing and Services and Research Institute for Smart Cities, Shenzhen University, Shenzhen 518060, China"},{"name":"School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Xu","sequence":"additional","affiliation":[{"name":"MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area and Guangdong Key Laboratory of Urban Informatics and Shenzhen Key Laboratory of Spatial Smart Sensing and Services and Research Institute for Smart Cities, Shenzhen University, Shenzhen 518060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7410-9541","authenticated-orcid":false,"given":"Kangyong","family":"Zhang","sequence":"additional","affiliation":[{"name":"MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area and Guangdong Key Laboratory of Urban Informatics and Shenzhen Key Laboratory of Spatial Smart Sensing and Services and Research Institute for Smart Cities, Shenzhen University, Shenzhen 518060, China"},{"name":"School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2689-3196","authenticated-orcid":false,"given":"Zhongwen","family":"Hu","sequence":"additional","affiliation":[{"name":"MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area and Guangdong Key Laboratory of Urban Informatics and Shenzhen Key Laboratory of Spatial Smart Sensing and Services and Research Institute for Smart Cities, Shenzhen University, Shenzhen 518060, China"},{"name":"School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guofeng","family":"Wu","sequence":"additional","affiliation":[{"name":"MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area and Guangdong Key Laboratory of Urban Informatics and Shenzhen Key Laboratory of Spatial Smart Sensing and Services and Research Institute for Smart Cities, Shenzhen University, Shenzhen 518060, China"},{"name":"School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wang, L., Fang, F., Yuan, X.H., Luo, Z.W., Liu, Y.Y., Wan, B., and Zhao, Y.S. (2017, January 23\u201328). Urban function zoning using geotagged photos and openstreetmap. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium, Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127077"},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1007\/s10618-012-0264-z","article-title":"Clustering daily patterns of human activities in the city","volume":"25","author":"Jiang","year":"2012","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4238","DOI":"10.1109\/TGRS.2015.2393857","article-title":"Effective and Efficient Midlevel Visual Elements-Oriented Land-Use Classification Using VHR Remote Sensing Images","volume":"53","author":"Gong","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","first-page":"339","article-title":"Remote sensing and GIS application in change detection study in urban zone using multi temporal satellite","volume":"4","author":"Manonmani","year":"2010","journal-title":"Int. J. Geomat. Geosci."},{"key":"ref_6","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_7","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.isprsjprs.2016.10.007","article-title":"Urban land use extraction from Very High Resolution remote sensing imagery using a Bayesian network","volume":"122","author":"Li","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_8","first-page":"1","article-title":"Using multi-level fusion of local features for land-use scene classification with high spatial resolution images in urban coastal zones","volume":"70","author":"Lu","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"712","DOI":"10.1109\/TKDE.2014.2345405","article-title":"Discovering urban functional zones using latent activity trajectories","volume":"27","author":"Yuan","year":"2015","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1080\/13658816.2016.1244608","article-title":"Sensing spatial distribution of urban land use by integrating points-of-interest and Google Word2Vec model","volume":"31","author":"Yao","year":"2016","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Long, Y., and Shen, Z. (2015). Discovering functional zones using bus smart card data and points of interest in Beijing. Geospatial Analysis to Support Urban Planning in Beijing, Springer.","DOI":"10.1007\/978-3-319-19342-7_10"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.landurbplan.2012.02.012","article-title":"Urban land uses and traffic \u2018source-sink areas\u2019: Evidence from GPS-enabled taxi data in Shanghai","volume":"106","author":"Yu","year":"2012","journal-title":"Landsc. Urban Plan."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2331","DOI":"10.1080\/13658816.2017.1356464","article-title":"Coupling mobile phone and social media data: A new approach to understanding urban functions and diurnal patterns","volume":"31","author":"Tu","year":"2017","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Yuan, Z., Li, Q., Huang, H., Wei, W., Xin, D., and Wang, H. (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_15","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, Q., Tu, W., Mai, K., Yao, Y., and Chen, Y. (2019). Functional urban land use recognition integrating multi-source geospatial data and cross-correlations. Comput. Environ. Urban Syst., 78.","DOI":"10.1016\/j.compenvurbsys.2019.101374"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Hu, T., Yang, J., Li, X., and Peng, G. (2016). Mapping urban land use by using landsat images and open social data. Remote Sens., 8.","DOI":"10.3390\/rs8020151"},{"key":"ref_17","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_18","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.rse.2018.05.006","article-title":"Integrating bottom-up classification and top-down feedback for improving urban land-cover and functional-zone mapping","volume":"212","author":"Zhang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1109\/TGRS.2014.2321423","article-title":"Features, Color Spaces, and Boosting: New insights on semantic classification of remote sensing images","volume":"53","author":"Tokarczyk","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","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_21","first-page":"993","article-title":"Latent dirichlet allocation","volume":"3","author":"Blei","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"446","DOI":"10.1111\/tgis.12289","article-title":"Extracting urban functional regions from points of interest and human activities on location-based social networks","volume":"21","author":"Song","year":"2017","journal-title":"Trans. GIS"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Schwalbach, G. (2017). Basics Urban Analysis, Birkh\u00e4user.","DOI":"10.1515\/9783035612851"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"146","DOI":"10.3390\/ijgi1020146","article-title":"Analyzing the contributor activity of a volunteered geographic information project \u00a1\u00aa The case of OpenStreetMap","volume":"1","author":"Neis","year":"2012","journal-title":"ISPRS Int. J. Geo-Inf."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/JSTARS.2018.2837222","article-title":"Semantic and spatial co-occurrence analysis on object pairs for urban scene classification","volume":"11","author":"Zhang","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.rse.2015.07.017","article-title":"A Linear dirichlet mixture model for decomposing scenes: Application to analyzing urban functional zonings","volume":"169","author":"Zhang","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_27","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 Syst."},{"key":"ref_28","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_29","doi-asserted-by":"crossref","unstructured":"Zhang, X., Du, S., Wang, Q., and Zhou, W. (2018). Multiscale geoscene segmentation for extracting urban functional zones from vhr satellite images. Remote Sens., 10.","DOI":"10.3390\/rs10020281"},{"key":"ref_30","first-page":"12","article-title":"What\u2019s wrong with pixels? Some recent developments interfacing remote sensing and GIS","volume":"14","author":"Blaschke","year":"2001","journal-title":"GIS\u2014Z. f\u00fcr Geoinf."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Tu, W., Zhang, Y., Li, Q., Mai, K., and Cao, J. (2020). Scale effect on fusing remote sensing and human sensing to portray urban functions. IEEE Geosci. Remote Sens. Lett.","DOI":"10.1109\/LGRS.2020.2965247"},{"key":"ref_32","first-page":"22","article-title":"Spatial pattern of urban functional landscapes along an urban\u2013rural gradient: A case study in Xiamen City, China","volume":"46","author":"Lin","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural features for image classification","volume":"SMC3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive Image Features from Scale-Invariant Keypoints. In Proceedings of International","volume":"60","author":"Lowe","year":"2004","journal-title":"J. Comput. Vis."},{"key":"ref_35","unstructured":"Ke, Y., Wang, Y., Liang, D., Huang, T., and Tian, Y. (2016, January 21\u201325). CNN vs. SIFT for Image Retrieval: Alternative or complementary?. Proceedings of the 24th ACM Multimedia Conference, Amsterdam, The Netherlands."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1109\/JSTARS.2008.2002869","article-title":"A Robust Built-Up Area Presence index by anisotropic rotation-invariant textural measure","volume":"1","author":"Pesaresi","year":"2009","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_37","first-page":"155","article-title":"Research on computation of GLCM of image texture","volume":"1","author":"Hua","year":"2006","journal-title":"Acta Electron. Sin."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1109\/TGRS.1986.289643","article-title":"textural information in sar images","volume":"24","author":"Ulaby","year":"1986","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","unstructured":"Yang, J., Kai, Y., Gong, Y., and Huang, T. (2009, January 20\u201325). Linear spatial pyramid matching using sparse coding for image classification. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zhao, F., Hao, S., Shuai, L., and Zhou, S. (2015, January 14). Combining low level features and visual attributes for VHR remote sensing image classification. Proceedings of the International Symposium on Multispectral Image Processing & Pattern Recognition, Enshi, China.","DOI":"10.1117\/12.2205566"},{"key":"ref_41","unstructured":"Ramage, D., Hall, D., Nallapati, R., and Manning, C.D. (2019, January 6\u20137). Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora. Proceedings of the Conference on Empirical Methods in Natural Language Processing, Singapore."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1109\/LGRS.2009.2023536","article-title":"Semantic Annotation of Satellite Images Using Latent Dirichlet Allocation","volume":"7","author":"Lienou","year":"2010","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"567","DOI":"10.14358\/PERS.83.8.567","article-title":"Unsupervised Deep Feature Learning for Urban Village Detection from High-Resolution Remote Sensing Images","volume":"83","author":"Li","year":"2017","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1016\/j.rse.2013.05.019","article-title":"Delineation of Central Business Districts in mega city regions using remotely sensed data","volume":"136","author":"Klotz","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Xing, H.F., and Meng, Y. (2020). Measuring urban landscapes for urban function classification using spatial metrics. Ecol. Indic., 108.","DOI":"10.1016\/j.ecolind.2019.105722"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2461","DOI":"10.1109\/JSTARS.2018.2833102","article-title":"Stepwise Evolution Analysis of the Region-Merging Segmentation for Scale Parameterization","volume":"11","author":"Hu","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.ecolind.2009.04.017","article-title":"Evaluating the effectiveness of landscape metrics in quantifying spatial patterns","volume":"10","author":"Peng","year":"2010","journal-title":"Ecol. Indic."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2017.2781712","article-title":"Scene Classification Based on the Sparse Homogeneous-Heterogeneous Topic Feature Model","volume":"56","author":"Zhu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","unstructured":"Xing, W., and Croft, W.B. (2007, January 13). LDA-based document models for ad-hoc retrieval. Proceedings of the International Acm Sigir Conference on Research Development in Information Retrieval, Piscataway, NJ, USA."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"6207","DOI":"10.1109\/TGRS.2015.2435801","article-title":"Scene classification based on the multifeature fusion probabilistic topic model for high spatial resolution remote sensing imagery","volume":"53","author":"Zhong","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/21\/3597\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:28:11Z","timestamp":1760178491000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/21\/3597"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,2]]},"references-count":50,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2020,11]]}},"alternative-id":["rs12213597"],"URL":"https:\/\/doi.org\/10.3390\/rs12213597","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,2]]}}}