{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T14:10:17Z","timestamp":1773843017709,"version":"3.50.1"},"reference-count":60,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2021,11,10]],"date-time":"2021-11-10T00:00:00Z","timestamp":1636502400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDA19040500"],"award-info":[{"award-number":["XDA19040500"]}]},{"name":"The Science and Technology Development Program of Jilin Province","award":["20200301014RQ"],"award-info":[{"award-number":["20200301014RQ"]}]},{"name":"The Youth Innovation Promotion Association, Chinese Academy of Sciences","award":["2017277"],"award-info":[{"award-number":["2017277"]}]},{"name":"The National Earth System Science Data Center","award":["www.geodata.cn"],"award-info":[{"award-number":["www.geodata.cn"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The urban functional area is critical to an understanding of the complex urban system, resource allocation, and management. However, due to urban surveys\u2019 focus on geographic objects and the mixture of urban space, it is difficult to obtain such information. The function of a place is determined by the activities that take place there. This study employed mobile phone signaling data to extract temporal features of human activities through discrete Fourier transform (DFT). Combined with the features extracted from the point of interest (POI) data and Sentinel images, the urban functional areas of Changchun City were identified using a random forest (RF) model. The results indicate that integrating features derived from remote sensing and social sensing data can effectively improve the identification accuracy and that features derived from dynamic mobile phone signaling have a higher identification accuracy than those derived from POI data. The human activity characteristics on weekends are more distinguishable for different functional areas than those on weekdays. The identified urban functional layout of Changchun is consistent with the actual situation. The residential functional area has the highest proportion, accounting for 33.51%, and is mainly distributed in the central area, while the industrial functional area and green-space are distributed around.<\/jats:p>","DOI":"10.3390\/rs13224512","type":"journal-article","created":{"date-parts":[[2021,11,11]],"date-time":"2021-11-11T23:04:46Z","timestamp":1636671886000},"page":"4512","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Identifying Urban Functional Areas in China\u2019s Changchun City from Sentinel-2 Images and Social Sensing Data"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4048-0091","authenticated-orcid":false,"given":"Shouzhi","family":"Chang","sequence":"first","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"},{"name":"School of Geomatics and Prospecting Engineering, Jilin Jianzhu University, Changchun 130118, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9865-8235","authenticated-orcid":false,"given":"Zongming","family":"Wang","sequence":"additional","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"},{"name":"National Earth System Science Data Center, Beijing 100101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3101-9153","authenticated-orcid":false,"given":"Dehua","family":"Mao","sequence":"additional","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}]},{"given":"Fusheng","family":"Liu","sequence":"additional","affiliation":[{"name":"Changchun Municipal Engineering Design and Research Institute, Changchun 130022, China"}]},{"given":"Lina","family":"Lai","sequence":"additional","affiliation":[{"name":"Changchun Municipal Engineering Design and Research Institute, Changchun 130022, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4043-2097","authenticated-orcid":false,"given":"Hao","family":"Yu","sequence":"additional","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"},{"name":"School of Geomatics and Prospecting Engineering, Jilin Jianzhu University, Changchun 130118, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhou, T., Liu, X., Qian, Z., Chen, H., and Tao, F. (2019). Automatic Identification of the Social Functions of Areas of Interest (AOIs) Using the Standard Hour-Day-Spectrum Approach. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9010007"},{"key":"ref_2","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."},{"key":"ref_3","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_4","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1016\/j.landurbplan.2012.01.001","article-title":"Urban structure type characterization using hyperspectral remote sensing and height information","volume":"105","author":"Heiden","year":"2012","journal-title":"Landsc. Urban Plan."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.landurbplan.2007.09.009","article-title":"People needs in the urban landscape: Analysis of Landscape and Urban Planning contributions","volume":"84","author":"Matsuoka","year":"2008","journal-title":"Landsc. Urban Plan."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1007\/s11769-017-0911-9","article-title":"China\u2019s urbanization in 1949\u20132015: Processes and driving forces","volume":"27","author":"Gu","year":"2017","journal-title":"Chin. Geogr. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"102976","DOI":"10.1016\/j.cities.2020.102976","article-title":"Health differences in an unequal city","volume":"108","year":"2021","journal-title":"Cities"},{"key":"ref_8","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_9","doi-asserted-by":"crossref","first-page":"964","DOI":"10.3390\/rs6020964","article-title":"Comparison of Classification Algorithms and Training Sample Sizes in Urban Land Classification with Landsat Thematic Mapper Imagery","volume":"6","author":"Li","year":"2014","journal-title":"Remote Sens."},{"key":"ref_10","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":"Gao","year":"2017","journal-title":"Trans. GIS"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1016\/j.compenvurbsys.2003.12.001","article-title":"The role of spatial metrics in the analysis and modeling of urban land use change","volume":"29","author":"Herold","year":"2005","journal-title":"Comput. Environ. Urban. Syst."},{"key":"ref_12","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. Am. Assoc. Geogr."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.compenvurbsys.2012.02.002","article-title":"Pervasive location acquisition technologies: Opportunities and challenges for geospatial studies","volume":"36","author":"Lu","year":"2012","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_14","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. Am. Assoc. Geogr."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.comcom.2020.06.005","article-title":"Uncovering spatiotemporal and semantic aspects of tourists mobility using social sensing","volume":"160","author":"Ferreira","year":"2020","journal-title":"Comput. Commun."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.compenvurbsys.2014.07.004","article-title":"Inferring building functions from a probabilistic model using public transportation data","volume":"48","author":"Chen","year":"2014","journal-title":"Comput. Environ. Urban. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Cui, H., Wu, L., Hu, S., Lu, R., and Wang, S. (2020). Recognition of Urban Functions and Mixed Use Based on Residents\u2019 Movement and Topic Generation Model: The Case of Wuhan, China. Remote Sens., 12.","DOI":"10.3390\/rs12182889"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.compenvurbsys.2016.10.004","article-title":"Putting people in the picture: Combining big location-based social media data and remote sensing imagery for enhanced contextual urban information in Shanghai","volume":"62","author":"Jendryke","year":"2017","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Dong, X., Xu, Y., Huang, L., Liu, Z., Xu, Y., Zhang, K., Hu, Z., and Wu, G. (2020). Exploring Impact of Spatial Unit on Urban Land Use Mapping with Multisource Data. Remote Sens., 12.","DOI":"10.3390\/rs12213597"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, Q., Huang, H., Wu, W., Du, X., 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_21","doi-asserted-by":"crossref","unstructured":"Wang, Y., Gu, Y., Dou, M., and Qiao, M. (2018). Using Spatial Semantics and Interactions to Identify Urban Functional Regions. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7040130"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"103157","DOI":"10.1016\/j.cities.2021.103157","article-title":"Discovering the evolution of urban structure using smart card data: The case of London","volume":"112","author":"Zhang","year":"2021","journal-title":"Cities"},{"key":"ref_23","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_24","first-page":"1339","article-title":"Identifying commuting pattern of Beijing using bus smart card data","volume":"67","author":"Long","year":"2012","journal-title":"Acta Geogr. Sin."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1109\/MCOM.2010.5560598","article-title":"A Survey of Mobile Phone Sensing","volume":"48","author":"Lane","year":"2010","journal-title":"IEEE Commun. Mag."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1080\/10630731003597306","article-title":"Using Mobile Positioning Data to Model Locations Meaningful to Users of Mobile Phones","volume":"17","author":"Ahas","year":"2010","journal-title":"J. Urban Technol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1080\/10630731003597322","article-title":"Does Urban Mobility Have a Daily Routine? Learning from the Aggregate Data of Mobile Networks","volume":"17","author":"Sevtsuk","year":"2010","journal-title":"J. Urban Technol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.trc.2015.02.018","article-title":"Origin\u2013destination trips by purpose and time of day inferred from mobile phone data","volume":"58","author":"Alexander","year":"2015","journal-title":"Transport. Res. C-Emer."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Tu, W., Hu, Z., Li, L., Cao, J., Jiang, J., Li, Q., and Li, 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":"1988","DOI":"10.1080\/13658816.2014.913794","article-title":"A new insight into land use classification based on aggregated mobile phone data","volume":"28","author":"Pei","year":"2014","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ma, Q., Gong, Z., Kang, J., Tao, R., and Dang, A. (2020). Measuring Functional Urban Shrinkage with Multi-Source Geospatial Big Data: A Case Study of the Beijing-Tianjin-Hebei Megaregion. Remote Sens., 12.","DOI":"10.3390\/rs12162513"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Chang, S., Jiang, Q., and Zhao, Y. (2018). Integrating CFD and GIS into the Development of Urban Ventilation Corridors: A Case Study in Changchun City, China. Sustainability, 10.","DOI":"10.3390\/su10061814"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"112285","DOI":"10.1016\/j.rse.2021.112285","article-title":"Rapid, robust, and automated mapping of tidal flats in China using time series Sentinel-2 images and Google Earth Engine","volume":"255","author":"Jia","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1023","DOI":"10.1080\/2150704X.2016.1212419","article-title":"Best practices for the reprojection and resampling of Sentinel-2 Multi Spectral Instrument Level 1C data","volume":"7","author":"Roy","year":"2016","journal-title":"Remote Sens. Lett."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zong, L., He, S., Lian, J., Bie, Q., Wang, X., Dong, J., and Xie, Y. (2020). Detailed Mapping of Urban Land Use Based on Multi-Source Data: A Case Study of Lanzhou. Remote Sens., 12.","DOI":"10.3390\/rs12121987"},{"key":"ref_36","unstructured":"(2021, January 21). The Sentinels Scientific Data Hub. Available online: https:\/\/scihub.copernicus.eu\/."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Chang, S., Wang, Z., Mao, D., Guan, K., Jia, M., and Chen, C. (2020). Mapping the Essential Urban Land Use in Changchun by Applying Random Forest and Multi-Source Geospatial Data. Remote Sens., 12.","DOI":"10.3390\/rs12152488"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2028","DOI":"10.18306\/dlkxjz.2020.12.006","article-title":"Characteristics of jobs-housing spatial distribution in Beijing based on mobile phone signaling data","volume":"39","author":"Wang","year":"2020","journal-title":"Prog. Geog."},{"key":"ref_39","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_40","doi-asserted-by":"crossref","unstructured":"Su, M., Guo, R., Chen, B., Hong, W., Wang, J., Feng, Y., and Xu, B. (2020). Sampling Strategy for Detailed Urban Land Use Classification: A Systematic Analysis in Shenzhen. Remote Sens., 12.","DOI":"10.3390\/rs12091497"},{"key":"ref_41","unstructured":"(2021, January 22). Introduction of Sen2cor. Available online: http:\/\/step.esa.int\/main\/snap-supported-plugins\/sen2cor\/."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1016\/j.scitotenv.2018.04.009","article-title":"Conversions between natural wetlands and farmland in China: A multiscale geospatial analysis","volume":"634","author":"Mao","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Wang, J., Hadjikakou, M., and Bryan, B.A. (2021). Consistent, accurate, high resolution, long time-series mapping of built-up land in the North China Plain. GISci. Remote Sens., 1\u201317.","DOI":"10.1080\/15481603.2021.1948275"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.isprsjprs.2020.03.020","article-title":"National wetland mapping in China: A new product resulting from object-based and hierarchical classification of Landsat 8 OLI images","volume":"164","author":"Mao","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_45","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_46","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/S0034-4257(00)00175-9","article-title":"Land-Surface Phenologies from AVHRR Using the Discrete Fourier Transform","volume":"75","author":"Moody","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1911","DOI":"10.1080\/014311600209814","article-title":"Reconstructing cloudfree NDVI composites using Fourier analysis of time series","volume":"21","author":"Roerink","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.rse.2017.11.009","article-title":"Remote sensing of mangrove forest phenology and its environmental drivers","volume":"205","author":"Dash","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"111839","DOI":"10.1016\/j.rse.2020.111839","article-title":"Training data requirements for fire severity mapping using Landsat imagery and random forest","volume":"245","author":"Collins","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1080\/01431160903252327","article-title":"Contextual land-cover classification: Incorporating spatial dependence in land-cover classification models using random forests and the Getis statistic","volume":"1","author":"Ghimire","year":"2010","journal-title":"Remote Sens. Lett."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Kranj\u010di\u0107, N., Medak, D., \u017dupan, R., and Rezo, M. (2019). Machine Learning Methods for Classification of the Green Infrastructure in City Areas. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8100463"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic Minority Over-sampling Technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Blagus, R., and Lusa, L. (2013). SMOTE for high-dimensional class-imbalanced data. BMC Bioinform., 14.","DOI":"10.1186\/1471-2105-14-106"},{"key":"ref_54","first-page":"6308","article-title":"Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review","volume":"13","author":"Sheykhmousa","year":"2020","journal-title":"IEEE J.-STARS"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1080\/2150704X.2014.882526","article-title":"High-resolution landcover classification using Random Forest","volume":"5","author":"Hayes","year":"2014","journal-title":"Remote Sens. Lett."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"4033","DOI":"10.1007\/s10489-019-01470-z","article-title":"A new hybrid feature selection based on multi-filter weights and multi-feature weights","volume":"49","author":"Wang","year":"2019","journal-title":"Appl. Intell."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"107080","DOI":"10.1016\/j.ecolind.2020.107080","article-title":"Exploring the equality of accessing urban green spaces: A comparative study of 341 Chinese cities","volume":"121","author":"Wu","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"106303","DOI":"10.1016\/j.ecoleng.2021.106303","article-title":"Spatially heterogeneity response of ecosystem services supply and demand to urbanization in China","volume":"169","author":"Pan","year":"2021","journal-title":"Ecol. Eng."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"89","DOI":"10.3390\/ijgi1010089","article-title":"Exploring Human Activity Patterns Using Taxicab Static Points","volume":"1","author":"Jia","year":"2012","journal-title":"ISPRS Int. J. Geo-Inf."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1559\/152304007782382918","article-title":"Mobile Positioning in Space\u2013Time Behaviour Studies: Social Positioning Method Experiments in Estonia","volume":"34","author":"Ahas","year":"2007","journal-title":"Cartogr. Geogr. Inf. Sci."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/22\/4512\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:28:09Z","timestamp":1760167689000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/22\/4512"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,10]]},"references-count":60,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["rs13224512"],"URL":"https:\/\/doi.org\/10.3390\/rs13224512","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,10]]}}}