{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T02:53:46Z","timestamp":1769914426369,"version":"3.49.0"},"reference-count":48,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,11,23]],"date-time":"2021-11-23T00:00:00Z","timestamp":1637625600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology","doi-asserted-by":"publisher","award":["2019YFC1510400"],"award-info":[{"award-number":["2019YFC1510400"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hong Kong Research Grants Council","award":["AoE\/E-603\/18"],"award-info":[{"award-number":["AoE\/E-603\/18"]}]},{"name":"Hong Kong Research Grants Council","award":["CRF C4139- 20G"],"award-info":[{"award-number":["CRF C4139- 20G"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Building function labelling plays an important role in understanding human activities inside buildings. This study develops a method of function label classification using integrated features derived from remote sensing and crowdsensing data with an extreme gradient boosting tree (XGBoost). The classification framework is verified based on a dataset from Shenzhen, China. An extended label system for six building types (residential, commercial, office, industrial, public facilities, and others) was applied, and various social functions were considered. The overall classification accuracies were 88.15% (kappa index = 0.72) and 85.56% (kappa index = 0.69). The importance of features was evaluated using the occurrence frequency of features at decision nodes. In the six-category classification system, the basic building attributes (22.99%) and POIs (46.74%) contributed most to the classification process; moreover, the building footprint (7.40%) and distance to roads (11.76%) also made notable contributions. The result shows that it is feasible to extract building environments from POI labels and building footprint geometry with a dimensional reduction model using an autoencoder. Additionally, crowdsensing data (e.g., POI and distance to roads) will become increasingly important as classification tasks become more complicated and the importance of basic building attributes declines.<\/jats:p>","DOI":"10.3390\/rs13234751","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T01:45:02Z","timestamp":1638323102000},"page":"4751","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Building Function Mapping Using Multisource Geospatial Big Data: A Case Study in Shenzhen, China"],"prefix":"10.3390","volume":"13","author":[{"given":"Jionghua","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4760-5958","authenticated-orcid":false,"given":"Haowen","family":"Luo","sequence":"additional","affiliation":[{"name":"Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3618-4337","authenticated-orcid":false,"given":"Wenyu","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5063-3522","authenticated-orcid":false,"given":"Bo","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong, China"},{"name":"Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong, China"},{"name":"Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen 518057, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,23]]},"reference":[{"key":"ref_1","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":"2014","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.rse.2014.08.024","article-title":"Object-based land cover mapping and comprehensive feature calculation for an automated derivation of urban structure types at block level","volume":"154","author":"Voltersen","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2447","DOI":"10.1080\/13658816.2018.1511793","article-title":"Are all cities with similar urban form or not? Redefining cities with ubiquitous points of interest and evaluating them with indicators at city and block levels in China","volume":"32","author":"Song","year":"2018","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_5","first-page":"1871","article-title":"Integrating multi-source big data to infer building functions","volume":"31","author":"Niu","year":"2017","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Hoffmann, E.J., Wang, Y., Werner, M., Kang, J., and Zhu, X.X. (2019). Model Fusion for Building Type Classification from Aerial and Street View Images. Remote Sens., 11.","DOI":"10.3390\/rs11111259"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1080\/17538947.2010.513114","article-title":"Mapping urban building stocks for vulnerability assessment\u2013preliminary results","volume":"4","author":"Saito","year":"2011","journal-title":"Int. J. Digit. Earth"},{"key":"ref_8","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_9","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.energy.2017.07.150","article-title":"Electricity load forecasting by an improved forecast engine for building level consumers","volume":"139","author":"Liu","year":"2017","journal-title":"Energy"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Newsham, G.R., and Birt, B.J. (2010, January 2). Building-level occupancy data to improve ARIMA-based electricity use forecasts. Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building, Zurich, Switzerland.","DOI":"10.1145\/1878431.1878435"},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1080\/01441647.2010.532883","article-title":"From macro to micro\u2014How much micro is too much?","volume":"31","author":"Wegener","year":"2011","journal-title":"Transp. Rev."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1109\/MCOM.2018.1700569","article-title":"Understanding urban human mobility through crowdsensed data","volume":"56","author":"Zhou","year":"2018","journal-title":"IEEE Commun. Mag."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1080\/17538947.2015.1037870","article-title":"An effective Building Neighborhood Green Index model for measuring urban green space","volume":"9","author":"Liu","year":"2016","journal-title":"Int. J. Digit. Earth"},{"key":"ref_15","unstructured":"International Energy Agency (2013). Directorate of Sustainable Energy Policy. Transition to Sustainable Buildings: Strategies and Opportunities to 2050, Organization for Economic."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"889","DOI":"10.1016\/j.apenergy.2017.09.060","article-title":"Machine learning approaches for estimating commercial building energy consumption","volume":"208","author":"Robinson","year":"2017","journal-title":"Appl. Energy"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1409","DOI":"10.1016\/j.enbuild.2011.02.002","article-title":"A systematic procedure to study the influence of occupant behavior on building energy consumption","volume":"43","author":"Yu","year":"2011","journal-title":"Energy Build."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"170001","DOI":"10.1038\/sdata.2017.1","article-title":"High resolution global gridded data for use in population studies","volume":"4","author":"Lloyd","year":"2017","journal-title":"Sci. Data"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1814","DOI":"10.1038\/s41467-019-09282-y","article-title":"New estimates of flood exposure in developing countries using high-resolution population data","volume":"10","author":"Smith","year":"2019","journal-title":"Nat. Commun."},{"key":"ref_20","first-page":"841","article-title":"Building population mapping with aerial imagery and GIS data","volume":"13","author":"Ural","year":"2011","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_21","first-page":"1220","article-title":"Mapping fine-scale population distributions at the building level by integrating multisource geospatial big data","volume":"31","author":"Yao","year":"2017","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1016\/j.rser.2013.05.057","article-title":"The city and urban heat islands: A review of strategies to mitigate adverse effects","volume":"25","author":"Gago","year":"2013","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1016\/j.regsciurbeco.2008.02.002","article-title":"Housing demand in Spain according to dwelling type: Microeconometric evidence","volume":"38","year":"2008","journal-title":"Reg. Sci. Urban Econ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"109051","DOI":"10.1016\/j.envres.2019.109051","article-title":"High-resolution assessment of road traffic noise exposure in Denmark","volume":"182","author":"Thacher","year":"2020","journal-title":"Environ. Res."},{"key":"ref_25","first-page":"46","article-title":"Building classification in Yangon City, Myanmar using Stereo GeoEye images, Landsat image and night-time light data","volume":"6","author":"Sritarapipat","year":"2017","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1323","DOI":"10.1007\/s11625-021-00923-0","article-title":"Does building development in Dhaka comply with land use zoning? An analysis using nighttime light and digital building heights","volume":"16","author":"Rahman","year":"2021","journal-title":"Sustain. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhuo, L., Shi, Q., Zhang, C., Li, Q., and Tao, H. (2019). Identifying building functions from the spatiotemporal population density and the interactions of people among buildings. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8060247"},{"key":"ref_28","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":"Zhong","year":"2014","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Srivastava, S., Vargas-Mu\u00f1oz, J.E., Swinkels, D., and Tuia, D. (2018, January 6). Multilabel Building Functions Classification from Ground Pictures using Convolutional Neural Networks. Proceedings of the 2nd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, Seattle, WA, USA.","DOI":"10.1145\/3281548.3281559"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.isprsjprs.2018.02.006","article-title":"Building instance classification using street view images","volume":"145","author":"Kang","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wurm, M., Taubenbock, H., Roth, A., and Dech, S. (2009, January 20\u201322). Urban structuring using multisensoral remote sensing data: By the example of the German cities Cologne and Dresden. Proceedings of the 2009 Joint Urban Remote Sensing Event, Shanghai, China.","DOI":"10.1109\/URS.2009.5137555"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_33","unstructured":"Goodfellow, I., Bengio, Y., Courville, A., and Bengio, Y. (2016). Deep Learning, MIT Press."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Cheng, Z., Sun, H., Takeuchi, M., and Katto, J. (2018, January 24\u201327). Deep convolutional autoencoder-based lossy image compression. Proceedings of the 2018 Picture Coding Symposium (PCS), San Francisco, CA, USA.","DOI":"10.1109\/PCS.2018.8456308"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1569","DOI":"10.1080\/13658816.2019.1584806","article-title":"Hierarchical community detection and functional area identification with OSM roads and complex graph theory","volume":"33","author":"Hong","year":"2019","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1543","DOI":"10.1177\/2399808319828730","article-title":"Evaluating and characterizing urban vibrancy using spatial big data: Shanghai as a case study","volume":"47","author":"Huang","year":"2020","journal-title":"Environ. Plan. B Urban Anal. City Sci."},{"key":"ref_37","first-page":"1457","article-title":"Non-negative matrix factorization with sparseness constraints","volume":"5","author":"Hoyer","year":"2004","journal-title":"J. Mach. Learn. Res."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/S0167-9473(01)00065-2","article-title":"Stochastic gradient boosting","volume":"38","author":"Friedman","year":"2002","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2001). The Elements of Statistical Learning. Springer Series in Statistics, Springer.","DOI":"10.1007\/978-0-387-21606-5"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3515","DOI":"10.1109\/JSTARS.2017.2686422","article-title":"Classification of Urban Building Type from High Spatial Resolution Remote Sensing Imagery Using Extended MRS and Soft BP Network","volume":"10","author":"Xie","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1111\/j.1467-9671.2008.01085.x","article-title":"An Approach for the Classification of Urban Building Structures Based on Discriminant Analysis Techniques","volume":"12","author":"Steiniger","year":"2008","journal-title":"Trans. GIS"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1016\/j.ijdrr.2017.07.006","article-title":"Classifying building occupancy using building laws and geospatial information: A case study in Bangkok","volume":"24","author":"Arunplod","year":"2017","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"ref_43","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1027","DOI":"10.1016\/j.rser.2017.09.108","article-title":"A review of data-driven approaches for prediction and classification of building energy consumption","volume":"82","author":"Wei","year":"2018","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_45","unstructured":"Oliveti, M. (2015). Analysis of Mobility Patterns in Different Neighbourhoods, Integrating GPS Tracks with OpenStreetMap Data. [Master\u2019s Thesis, Delft University of Technology]."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1007\/s00704-020-03298-x","article-title":"Refined dataset to describe the complex urban environment of Hong Kong for urban climate modelling studies at the mesoscale","volume":"142","author":"Kwok","year":"2020","journal-title":"Theor. Appl. Climatol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1807","DOI":"10.21105\/joss.01807","article-title":"MOMEPY: Urban morphology measuring toolkit","volume":"4","author":"Fleischmann","year":"2019","journal-title":"J. Open Source Softw."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1077","DOI":"10.1080\/17538947.2016.1269841","article-title":"Building segmentation and outline extraction from UAV image-derived point clouds by a line growing algorithm","volume":"10","author":"Dai","year":"2017","journal-title":"Int. J. Digit. Earth"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/23\/4751\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:34:50Z","timestamp":1760168090000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/23\/4751"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,23]]},"references-count":48,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["rs13234751"],"URL":"https:\/\/doi.org\/10.3390\/rs13234751","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,23]]}}}