{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T17:25:18Z","timestamp":1782840318573,"version":"3.54.5"},"reference-count":89,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,10,22]],"date-time":"2021-10-22T00:00:00Z","timestamp":1634860800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Major Program of the National Natural Science Foundation of China","award":["20201320003"],"award-info":[{"award-number":["20201320003"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41801163"],"award-info":[{"award-number":["41801163"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012456","name":"National Social Science Foundation of China","doi-asserted-by":"publisher","award":["21AZD034"],"award-info":[{"award-number":["21AZD034"]}],"id":[{"id":"10.13039\/501100012456","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the University of Hong Kong HKU-100 Scholars Fund","award":["\/"],"award-info":[{"award-number":["\/"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Detailed information on urban land uses has been an essential requirement for urban land management and policymaking. Recent advances in remote sensing and machine learning technologies have contributed to the mapping and monitoring of multi-scale urban land uses, yet there lacks a holistic mapping framework that is compatible with different end users\u2019 demands. Moreover, land use mix has evolved to be a key component in modern urban settings, but few have explicitly measured the spatial complexity of land use or quantitively uncovered its driving forces. Addressing these challenges, here we developed a novel two-stage bottom-up scheme for mapping essential urban land use categories. In the first stage, we conducted object-based land use classification using crowdsourcing features derived from multi-source open big data and an automated ensemble learning approach. In the second stage, we identified parcel-based land use attributes, including the dominant type and mixture mode, by spatially correlating land parcels with the object-based results. Furthermore, we investigated the potential influencing factors of land use mix using principal components analysis and multiple linear regression. Experimental results in Ningbo, a coastal city in China, showed that the proposed framework could accurately depict the distribution and composition of urban land uses. At the object scale, the highest classification accuracy was as high as 86% and 78% for the major (Level I) and minor (Level II) categories, respectively. At the parcel scale, the generated land use maps were spatially consistent with the object-based maps. We found larger parcels were more likely to be mixed in land use, and industrial lands were characterized as the most complicated category. We also identified multiple factors that had a collective impact on land use mix, including geography, socioeconomy, accessibility, and landscape metrics. Altogether, our proposed framework offered an alternative to investigating urban land use composition, which could be applied in a broad range of implications in future urban studies.<\/jats:p>","DOI":"10.3390\/rs13214241","type":"journal-article","created":{"date-parts":[[2021,10,24]],"date-time":"2021-10-24T22:07:11Z","timestamp":1635113231000},"page":"4241","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Uncovering the Nature of Urban Land Use Composition Using Multi-Source Open Big Data with Ensemble Learning"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2240-5389","authenticated-orcid":false,"given":"Ying","family":"Tu","sequence":"first","affiliation":[{"name":"Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Chen","sequence":"additional","affiliation":[{"name":"Division of Landscape Architecture, Faculty of Architecture, The University of Hong Kong, Hong Kong SAR, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Lang","sequence":"additional","affiliation":[{"name":"Department of Urban and Regional Planning, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China"},{"name":"China Regional Coordinated Development and Rural Construction Institute, Sun Yat-sen University, Guangzhou 510275, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tingting","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Urban and Regional Planning, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China"},{"name":"China Regional Coordinated Development and Rural Construction Institute, Sun Yat-sen University, Guangzhou 510275, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miao","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bing","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"111510","DOI":"10.1016\/j.rse.2019.111510","article-title":"Annual Maps of Global Artificial Impervious Area (GAIA) between 1985 and 2018","volume":"236","author":"Gong","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_2","unstructured":"United Nations, Department of Economic and Social Affairs, Population Division (2019). World Population Prospects 2019: Highlights, United Nations."},{"key":"ref_3","unstructured":"United Nations (2021, August 20). World Urbanization Prospects 2018: Highlights. Available online: https:\/\/population.un.org\/wup\/Publications\/Files\/WUP2018-Highlights.pdf."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"756","DOI":"10.1126\/science.1150195","article-title":"Global Change and the Ecology of Cities","volume":"319","author":"Grimm","year":"2008","journal-title":"Science"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.rse.2017.02.027","article-title":"Environmental degradation in the urban areas of China: Evidence from multi-source remote sensing data","volume":"193","author":"He","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"956","DOI":"10.1016\/j.scitotenv.2017.07.238","article-title":"Quantitative estimation of 21st-century urban greenspace changes in Chinese populous cities","volume":"609","author":"Chen","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"119018","DOI":"10.1016\/j.jclepro.2019.119018","article-title":"How does urban expansion impact people\u2019s exposure to green environments? A comparative study of 290 Chinese cities","volume":"246","author":"Song","year":"2020","journal-title":"J. Clean. Prod."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1007\/s10980-020-01137-y","article-title":"How does urban expansion interact with cropland loss? A comparison of 14 Chinese cities from 1980 to 2015","volume":"36","author":"Tu","year":"2021","journal-title":"Landsc. Ecol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"755","DOI":"10.1038\/s41893-019-0340-0","article-title":"Direct and Indirect Loss of Natural Area from Urban Expansion","volume":"2","year":"2019","journal-title":"Nat. Sustain."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1695","DOI":"10.1016\/j.biocon.2008.04.025","article-title":"The Implications of Current and Future Urbanization for Global Protected Areas and Biodiversity Conservation","volume":"141","author":"McDonald","year":"2008","journal-title":"Biol. Conserv."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1641\/0006-3568(2002)052[0883:UBAC]2.0.CO;2","article-title":"Urbanization, Biodiversity, and ConservationThe Impacts of Urbanization on Native Species are Poorly Studied, but Educating a Highly Urbanized Human Population about These Impacts can Greatly Improve Species Conservation in all Ecosystems","volume":"52","author":"McKinney","year":"2002","journal-title":"Bioscience"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.isprsjprs.2021.06.010","article-title":"Mapping Essential Urban Land use Categories with Open Big Data: Results for Five Metropolitan Areas in the United States of America","volume":"178","author":"Chen","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"410","DOI":"10.1080\/20964471.2021.1939243","article-title":"Mapping Essential Urban Land Use Categories (Euluc) Using Geospatial Big Data: Progress, Challenges, and Opportunities","volume":"5","author":"Chen","year":"2021","journal-title":"Big Earth Data"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1459","DOI":"10.1080\/01431169208904202","article-title":"Land-Use Classification of SPOT HRV Data Using a Cover-Frequency Method","volume":"13","author":"Gong","year":"1992","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/0034-4257(92)90011-8","article-title":"A Comparison of Spatial Feature Extraction Algorithms for Land-Use Classification with SPOT HRV Data","volume":"40","author":"Gong","year":"1992","journal-title":"Remote Sens. Environ."},{"key":"ref_16","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":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1403","DOI":"10.14358\/PERS.73.12.1403","article-title":"Employing Spatial Metrics in Urban Land-Use\/Land-Cover Mapping","volume":"73","author":"Myint","year":"2007","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1276","DOI":"10.1016\/j.rse.2009.02.014","article-title":"A Neural Network Approach Using Multi-Scale Textural Metrics from Very High-Resolution Panchromatic Imagery for Urban land-Use Classification","volume":"113","author":"Pacifici","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Theobald, D.M. (2014). Development and Applications of a Comprehensive Land Use Classification and Map for the US. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0094628"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"991","DOI":"10.14358\/PERS.69.9.991","article-title":"Spatial Metrics and Image Texture for Mapping Urban Land Use","volume":"69","author":"Herold","year":"2003","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.cageo.2011.08.019","article-title":"Support Vector Machines and Object-Based Classification for Obtaining Land-Use\/Cover Cartography from Hyperion Hyperspectral Imagery","volume":"41","author":"Petropoulos","year":"2012","journal-title":"Comput. Geosci."},{"key":"ref_22","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":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Tu, Y., Chen, B., Zhang, T., and Xu, B. (2020). Regional Mapping of Essential Urban Land Use Categories in China: A Segmentation-Based Approach. Remote Sens., 12.","DOI":"10.3390\/rs12071058"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.isprsjprs.2009.06.004","article-title":"Object Based Image Analysis for Remote Sensing","volume":"65","author":"Blaschke","year":"2010","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1080\/01431161003743173","article-title":"Assessing Object-Based Classification: Advantages and Limitations","volume":"1","author":"Liu","year":"2010","journal-title":"Remote Sens. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep Learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep Learning in Neural Networks: An Overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.isprsjprs.2019.04.015","article-title":"Deep Learning in Remote Sensing Applications: A Meta-Analysis and Review","volume":"152","author":"Ma","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.rse.2018.04.050","article-title":"Urban Land-Use Mapping Using a Deep Convolutional Neural Network with High Spatial Resolution Multispectral Remote Sensing Imagery","volume":"214","author":"Huang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.rse.2018.06.034","article-title":"An Object-Based Convolutional Neural Network (OCNN) for Urban Land Use Classification","volume":"216","author":"Zhang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, Z., Li, X., and Yeh, A.G.-O. (2019). Integration of Convolutional Neural Networks and Object-Based Post-Classification Refinement for Land Use and Land Cover Mapping with Optical and SAR Data. Remote Sens., 11.","DOI":"10.3390\/rs11060690"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.rse.2019.04.014","article-title":"Understanding Urban Landuse from the Above and Ground Perspectives: A Deep Learning, Multimodal Solution","volume":"228","author":"Srivastava","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.rse.2018.11.014","article-title":"Joint Deep Learning for Land Cover and Land Use Classification","volume":"221","author":"Zhang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Bao, H., Ming, D., Guo, Y., Zhang, K., Zhou, K., and Du, S. (2020). DFCNN-Based Semantic Recognition of Urban Functional Zones by Integrating Remote Sensing Data and POI Data. Remote Sens., 12.","DOI":"10.3390\/rs12071088"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Atwell, W., Rojdev, K., Aghara, S., and Sriprisan, S. (2013). Mitigating the Effects of the Space Radiation Environment: A Novel Approach of Using Graded-Z Materials. AIAA SPACE 2013 Conference & Exposition, American Institute of Aeronautics and Astronautics.","DOI":"10.2514\/6.2013-5385"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1016\/j.rse.2010.12.017","article-title":"Per-Pixel Vs. Object-Based Classification of Urban Land Cover Extraction Using High Spatial Resolution Imagery","volume":"115","author":"Myint","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"111838","DOI":"10.1016\/j.rse.2020.111838","article-title":"Open-Source Data-Driven Urban Land-Use Mapping Integrating Point-Line-Polygon Semantic Objects: A Case Study of Chinese Cities","volume":"247","author":"Zhong","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1229","DOI":"10.1080\/01431160512331326800","article-title":"A Per-Field Classification Method Based on Mixture Distribution Models and an Application to Landsat Thematic Mapper Data","volume":"26","author":"Erol","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1177\/0265813515604767","article-title":"Automated Identification and Characterization of Parcels with OpenStreetMap and Points of Interest","volume":"43","author":"Liu","year":"2015","journal-title":"Environ. Plan. B Plan. Des."},{"key":"ref_40","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_41","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.scib.2019.12.007","article-title":"Mapping Essential Urban Land Use Categories in China (EULUC-China): Preliminary Results for 2018","volume":"65","author":"Gong","year":"2020","journal-title":"Sci. Bull."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Polikar, R. (2012). Ensemble Learning. Ensemble Machine Learning, Springer.","DOI":"10.1007\/978-1-4419-9326-7_1"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1249","DOI":"10.1002\/widm.1249","article-title":"Ensemble Learning: A Survey","volume":"8","author":"Sagi","year":"2018","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"112364","DOI":"10.1016\/j.rse.2021.112364","article-title":"Production of Global Daily Seamless Data Cubes and Quantification of Global Land Cover Change from 1985 to 2020\u2014Imap World 1.0","volume":"258","author":"Liu","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"4973","DOI":"10.1109\/JSTARS.2020.3019410","article-title":"Semi-MCNN: A Semisupervised Multi-CNN Ensemble Learning Method for Urban Land Cover Classification Using Submeter HRRS Images","volume":"13","author":"Fan","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_46","first-page":"102164","article-title":"Mapping Wetland Using the Object-Based Stacked Generalization Method Based on Multi-temporal Optical and SAR Data","volume":"92","author":"Cai","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Wen, L., and Hughes, M. (2020). Coastal Wetland Mapping Using Ensemble Learning Algorithms: A Comparative Study of Bagging, Boosting and Stacking Techniques. Remote Sens., 12.","DOI":"10.3390\/rs12101683"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Wang, X., Liu, S., Du, P., Liang, H., Xia, J., and Li, Y. (2018). Object-Based Change Detection in Urban Areas from High Spatial Resolution Images Based on Multiple Features and Ensemble Learning. Remote Sens., 10.","DOI":"10.3390\/rs10020276"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Cui, B., Zhang, Y., Yan, L., Wei, J., and Wu, H. (2019). An Unsupervised SAR Change Detection Method Based on Stochastic Subspace Ensemble Learning. Remote Sens., 11.","DOI":"10.3390\/rs11111314"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compenvurbsys.2013.08.001","article-title":"Comparing Measures of Urban Land Use Mix","volume":"42","author":"Song","year":"2013","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.landusepol.2017.09.036","article-title":"Measuring Residential and Industrial Land Use Mix in the Peri-Urban Areas of China","volume":"69","author":"Tian","year":"2017","journal-title":"Land Use Policy"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1080\/15481603.2014.993854","article-title":"GIS-Based Modeling for the Spatial Measurement and Evaluation of Mixed Land Use Development for a Compact City","volume":"52","author":"Abdullahi","year":"2015","journal-title":"GIScience Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"6357","DOI":"10.1109\/TGRS.2020.3028622","article-title":"Accurate Estimation of the Proportion of Mixed Land Use at the Street-Block Level by Integrating High Spatial Resolution Images and Geospatial Big Data","volume":"59","author":"He","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.compenvurbsys.2019.01.005","article-title":"Compatibility Mix Degree Index: A Novel Measure to Characterize Urban Land Use Mix Pattern","volume":"75","author":"Zhuo","year":"2019","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Jacobs, J. (2016). The Death and Life of Great American Cities, Vintage.","DOI":"10.1002\/9781119084679.ch4"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1023\/A:1017959825565","article-title":"A Micro-Analysis of Land Use and Travel in Five Neighborhoods in the San Francisco Bay Area","volume":"24","author":"Kitamura","year":"1997","journal-title":"Transportation"},{"key":"ref_57","first-page":"782","article-title":"Relationships of Land Use Mix with Walking for Transport: Do Land Uses and Geographical Scale Matter?","volume":"87","author":"Duncan","year":"2010","journal-title":"J. Hered."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"13098","DOI":"10.1111\/obr.13098","article-title":"Land Use Mix in the Neighbourhood and Childhood Obesity","volume":"22","author":"Jia","year":"2021","journal-title":"Obes. Rev."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.amepre.2004.11.001","article-title":"Linking Objectively Measured Physical Activity with Objectively Measured Urban form: Findings from SMARTRAQ","volume":"28","author":"Frank","year":"2005","journal-title":"Am. J. Prev. Med."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.cities.2015.01.003","article-title":"Paths to Mixed-Use Development: A Case Study of Southern Changping in Beijing, China","volume":"44","author":"Kong","year":"2015","journal-title":"Cities"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.apgeog.2015.02.015","article-title":"The Development and Application of a Land Use Diversity Index for Oklahoma City, OK","volume":"60","author":"Comer","year":"2015","journal-title":"Appl. Geogr."},{"key":"ref_62","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_63","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.scib.2019.03.002","article-title":"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","volume":"64","author":"Gong","year":"2019","journal-title":"Sci. Bull."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Achanta, R., and Susstrunk, S. (2017, January 21\u201326). Superpixels and Polygons Using Simple Non-Iterative Clustering. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.520"},{"key":"ref_65","unstructured":"Bhabatosh, C. (1977). Digital Image Processing and Analysis, PHI Learning Pvt. Ltd."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"529","DOI":"10.14358\/PERS.69.5.529","article-title":"Comparison of Gray-Level Reduction and Different Texture Spectrum Encoding Methods for Land-Use Classification Using a Panchromatic Ikonos Image","volume":"69","author":"Xu","year":"2003","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1080\/00045600802459028","article-title":"Using Geometrical, Textural, and Contextual Information of Land Parcels for Classification of Detailed Urban Land Use","volume":"99","author":"Wu","year":"2009","journal-title":"Ann. Assoc. Am. Geogr."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural Features for Image Classification","volume":"SMC-3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Kupidura, P. (2019). The Comparison of Different Methods of Texture Analysis for Their Efficacy for Land Use Classification in Satellite Imagery. Remote Sens., 11.","DOI":"10.3390\/rs11101233"},{"key":"ref_70","unstructured":"Erickson, N., Mueller, J., Shirkov, A., Zhang, H., Larroy, P., Li, M., and Smola, A. (2020). Autogluon-Tabular: Robust and Accurate Automl for Structured Data. arXiv."},{"key":"ref_71","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_72","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","article-title":"Extremely Randomized Trees","volume":"63","author":"Geurts","year":"2006","journal-title":"Mach. Learn."},{"key":"ref_73","unstructured":"Dorogush, A.V., Ershov, V., and Gulin, A. (2018). CatBoost: Gradient Boosting with Categorical Features Support. arXiv."},{"key":"ref_74","unstructured":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y. (2021, August 20). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Available online: https:\/\/proceedings.neurips.cc\/paper\/2017\/file\/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf."},{"key":"ref_75","unstructured":"Yegnanarayana, B. (2009). Artificial Neural Networks, PHI Learning Pvt. Ltd."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0034-4257(91)90048-B","article-title":"A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data","volume":"37","author":"Congalton","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Congalton, R.G., and Green, K. (2019). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, CRC Press.","DOI":"10.1201\/9780429052729"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Jiao, J., Rollo, J., and Fu, B. (2021). The Hidden Characteristics of Land-Use Mix Indices: An Overview and Validity Analysis Based on the Land Use in Melbourne, Australia. Sustainability, 13.","DOI":"10.3390\/su13041898"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"1130","DOI":"10.1111\/tgis.12447","article-title":"A Dynamic Human Activity-Driven Model for Mixed Land Use Evaluation Using Social Media Data","volume":"22","author":"Xing","year":"2018","journal-title":"Trans. GIS"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1016\/j.landusepol.2018.08.031","article-title":"Rediscovering Chinese Cities through the Lens of Land-Use Patterns","volume":"79","author":"Lang","year":"2018","journal-title":"Land Use Policy"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"658","DOI":"10.1080\/13658816.2016.1220561","article-title":"Measurements of POI-Based Mixed Use and Their Relationships with Neighbourhood Vibrancy","volume":"31","author":"Yue","year":"2017","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.cities.2014.10.001","article-title":"City profile: Ningbo","volume":"42","author":"Tang","year":"2015","journal-title":"Cities"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1016\/j.asr.2019.09.035","article-title":"GDP Spatialization in Ningbo City based on NPP\/VIIRS Night-Time Light and Auxiliary Data Using Random Forest Regression","volume":"65","author":"Liang","year":"2020","journal-title":"Adv. Space Res."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.landusepol.2016.01.016","article-title":"Socioeconomic Drivers of Forest Loss and Fragmentation: A Comparison between Different Land Use Planning Schemes and Policy Implications","volume":"54","author":"Liu","year":"2016","journal-title":"Land Use Policy"},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Han, Y., Yu, C., Feng, Z., Du, H., Huang, C., and Wu, K. (2021). Construction and Optimization of Ecological Security Pattern Based on Spatial Syntax Classification\u2014Taking Ningbo, China, as an Example. Land, 10.","DOI":"10.3390\/land10040380"},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Zhang, C., Zhong, S., Wang, X., Shen, L., Liu, L., and Liu, Y. (2019). Land Use Change in Coastal Cities during the Rapid Urbanization Period from 1990 to 2016: A Case Study in Ningbo City, China. Sustainability, 11.","DOI":"10.3390\/su11072122"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.isprsjprs.2016.12.008","article-title":"Multi-Source Remotely Sensed Data Fusion for Improving Land Cover Classification","volume":"124","author":"Chen","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"2359","DOI":"10.1109\/LGRS.2015.2453999","article-title":"Fine Land Cover Classification Using Daily Synthetic Landsat-Like Images at 15-m Resolution","volume":"12","author":"Chen","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"5384","DOI":"10.1109\/JSTARS.2020.3022210","article-title":"Improved Mapping Results of 10 m Resolution Land Cover Classification in Guangdong, China Using Multisource Remote Sensing Data With Google Earth Engine","volume":"13","author":"Tu","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/21\/4241\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:20:58Z","timestamp":1760167258000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/21\/4241"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,22]]},"references-count":89,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["rs13214241"],"URL":"https:\/\/doi.org\/10.3390\/rs13214241","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,22]]}}}