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Arsanjani, M. Helbich, M. Bakillah, J. Hagenauer, and A. Zipf, \u201cToward mapping land-use patterns from volunteered geographic information,\u201d International Journal of Geographical Information Science, vol.27, no.12, pp.2264-2278, 2013. 10.1080\/13658816.2013.800871","DOI":"10.1080\/13658816.2013.800871"},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] R. Ahas, A. Aasa, Y. Yuan, M. Raubal, Z. Smoreda, Y. Liu, C. Ziemlicki, M. Tiru, and M. Zook, \u201cEveryday space-time geographies: using mobile phone-based sensor data to monitor urban activity in harbin, paris, and tallinn,\u201d International Journal of Geographical Information Science, vol.29, no.11, pp.2017-2039, 2015. 10.1080\/13658816.2015.1063151","DOI":"10.1080\/13658816.2015.1063151"},{"key":"3","doi-asserted-by":"publisher","unstructured":"[3] X. Liu, J. He, Y. Yao, J. Zhang, H. Liang, H. Wang, and Y. Hong, \u201cClassifying urban land use by integrating remote sensing and social media data,\u201d International Journal of Geographical Information Science, vol.31, no.8, pp.1675-1696, 2017. 10.1080\/13658816.2017.1324976","DOI":"10.1080\/13658816.2017.1324976"},{"key":"4","doi-asserted-by":"publisher","unstructured":"[4] S. Hu and L. Wang, \u201cAutomated urban land-use classification with remote sensing,\u201d International Journal of Remote Sensing, vol.34, no.3, pp.790-803, 2013. 10.1080\/01431161.2012.714510","DOI":"10.1080\/01431161.2012.714510"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] X. Huang, Q. Lu, and L. Zhang, \u201cA multi-index learning approach for classification of high-resolution remotely sensed images over urban areas,\u201d ISPRS Journal of Photogrammetry and Remote Sensing, vol.90, pp.36-48, 2014. 10.1016\/j.isprsjprs.2014.01.008","DOI":"10.1016\/j.isprsjprs.2014.01.008"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] U. Heiden, W. Heldens, S. Roessner, K. Segl, T. Esch, and A. Mueller, \u201cUrban structure type characterization using hyperspectral remote sensing and height information,\u201d Landscape and urban Planning, vol.105, no.4, pp.361-375, 2012. 10.1016\/j.landurbplan.2012.01.001","DOI":"10.1016\/j.landurbplan.2012.01.001"},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] T. Van de Voorde, W. Jacquet, and F. Canters, \u201cMapping form and function in urban areas: An approach based on urban metrics and continuous impervious surface data,\u201d Landscape and Urban Planning, vol.102, no.3, pp.143-155, 2011. 10.1016\/j.landurbplan.2011.03.017","DOI":"10.1016\/j.landurbplan.2011.03.017"},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] R. Cao, W. Tu, J. Cao, and Q. Li, \u201cComparison of urban human movements inferring from multi-source spatial-temporal data,\u201d ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol.XLI-B2, pp.471-476, 2016. 10.5194\/isprsarchives-xli-b2-471-2016","DOI":"10.5194\/isprsarchives-XLI-B2-471-2016"},{"key":"9","unstructured":"[9] J. Cao, W. Tu, Q. Li, and R. Cao, \u201cSpatio-temporal analysis of aggregated human activities based on massive mobile phone tracking data,\u201d Journal of Geo-Information Science, vol.19, no.4, pp.467-474, 2017."},{"key":"10","doi-asserted-by":"publisher","unstructured":"[10] G. Pan, G. Qi, Z. Wu, D. Zhang, and S. Li, \u201cLand-use classification using taxi gps traces,\u201d IEEE Transactions on Intelligent Transportation Systems, vol.14, no.1, pp.113-123, 2012. 10.1109\/tits.2012.2209201","DOI":"10.1109\/TITS.2012.2209201"},{"key":"11","doi-asserted-by":"publisher","unstructured":"[11] Y. Yue, Y. Zhuang, A. Yeh, J.-Y. Xie, and Q.Q. Li, \u201cMeasurements of poi-based mixed use and their relationships with neighbourhood vibrancy,\u201d International Journal of Geographical Information Systems, vol.31, no.4, pp.658-675, 2017. 10.1080\/13658816.2016.1220561","DOI":"10.1080\/13658816.2016.1220561"},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] S. Jiang, A. Alves, F. Rodrigues, J. Ferreira Jr., and F.C. Pereira, \u201cMining point-of-interest data from social networks for urban land use classification and disaggregation,\u201d Computers, Environment and Urban Systems, vol.53, pp.36-46, 2015. 10.1016\/j.compenvurbsys.2014.12.001","DOI":"10.1016\/j.compenvurbsys.2014.12.001"},{"key":"13","doi-asserted-by":"publisher","unstructured":"[13] T. Pei, S. Sobolevsky, C. Ratti, S.-L. Shaw, T. Li, and C. Zhou, \u201cA new insight into land use classification based on aggregated mobile phone data,\u201d International Journal of Geographical Information Science, vol.28, no.9, pp.1988-2007, 2014. 10.1080\/13658816.2014.913794","DOI":"10.1080\/13658816.2014.913794"},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] X. Huang, R. Li, B. Wang, and L. Li, \u201cClassification of the functions of urban area based on deep learning,\u201d Proceedings of the 2nd International Conference on Information Technologies and Electrical Engineering, ser. ICITEE-2019. New York, NY, USA: Association for Computing Machinery, pp.1-5, 2019. 10.1145\/3386415.3387103","DOI":"10.1145\/3386415.3387103"},{"key":"15","doi-asserted-by":"publisher","unstructured":"[15] R. Cao, W. Tu, C. Yang, Q. Li, J. Liu, J. Zhu, Q. Zhang, Q. Li, and G. Qiu, \u201cDeep learning-based remote and social sensing data fusion for urban region function recognition,\u201d ISPRS Journal of Photogrammetry and Remote Sensing, vol.163, pp.82-97, 2020. 10.1016\/j.isprsjprs.2020.02.014","DOI":"10.1016\/j.isprsjprs.2020.02.014"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] N. Audebert, B. Le Saux, and S. Lef\u00e8vrey, \u201cFusion of heterogeneous data in convolutional networks for urban semantic labeling,\u201d 2017 Joint Urban Remote Sensing Event (JURSE), pp.1-4, 2017. 10.1109\/jurse.2017.7924566","DOI":"10.1109\/JURSE.2017.7924566"},{"key":"17","doi-asserted-by":"publisher","unstructured":"[17] C. Qiu, L. Mou, M. Schmitt, and X. Zhu, \u201cFusing multiseasonal Sentinel-2 imagery for urban land cover classification with multibranch residual convolutional neural networks,\u201d IEEE Geoscience and Remote Sensing Letters, vol.17, no.10, pp.1787-1791, 2020. 10.1109\/lgrs.2019.2953497","DOI":"10.1109\/LGRS.2019.2953497"},{"key":"18","doi-asserted-by":"publisher","unstructured":"[18] C. Chen, J. Yan, L. Wang, D. Liang, and W. Zhang, \u201cClassification of urban functional areas from remote sensing images and time-series user behaviour data,\u201d IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.14, pp.1207-1221, 2020. 10.1109\/jstars.2020.3044250","DOI":"10.1109\/JSTARS.2020.3044250"},{"key":"19","doi-asserted-by":"crossref","unstructured":"[19] Z. Wang, Y. Li, Y. Guo, L. Fang, and S. 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Qiao, \u201cSuppressing uncertainties for large-scale facial expression recognition,\u201d Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp.6897-6906, 2020. 10.1109\/cvpr42600.2020.00693","DOI":"10.1109\/CVPR42600.2020.00693"},{"key":"23","unstructured":"[23] B. Han, Q. Yao, X. Yu, G. Niu, M. Xu, W. Hu, I. Tsang, and M. Sugiyama, \u201cCombating noisy labels by agreement: A joint training method with co-regularization,\u201d arXiv preprint arXiv:1804.06872, 2018."},{"key":"24","doi-asserted-by":"crossref","unstructured":"[24] H. Wei, L. Feng, X. Chen, and B. An, \u201cCombating noisy labels by agreement: A joint training method with co-regularization,\u201d Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp.13726-13735, 2020. 10.1109\/cvpr42600.2020.01374","DOI":"10.1109\/CVPR42600.2020.01374"},{"key":"25","doi-asserted-by":"publisher","unstructured":"[25] K. Cockx, T. Van de Voorde, and F. Canters, \u201cQuantifying uncertainty in remote sensing-based urban land-use mapping,\u201d International Journal of Applied Earth Observation and Geoinformation, vol.31, pp.154-166, 2014. 10.1016\/j.jag.2014.03.016","DOI":"10.1016\/j.jag.2014.03.016"},{"key":"26","doi-asserted-by":"publisher","unstructured":"[26] Y. Yao, X. Li, X. Liu, P. Liu, Z. Liang, J. Zhang, and K. Mai, \u201cSensing spatial distribution of urban land use by integrating points-of-interest and google word2vec model,\u201d International Journal of Geographical Information Science, vol.31, no.4, pp.825-848, 2017. 10.1080\/13658816.2016.1244608","DOI":"10.1080\/13658816.2016.1244608"},{"key":"27","doi-asserted-by":"publisher","unstructured":"[27] Q. Gao, J. Fu, Y. Yu, and X. Tang, \u201cIdentification of urban regions` functions in chengdu, china, based on vehicle trajectory data,\u201d PloS one, vol.14, no.4, p.e0215656, 2019. 10.1371\/journal.pone.0215656","DOI":"10.1371\/journal.pone.0215656"},{"key":"28","doi-asserted-by":"publisher","unstructured":"[28] Yuyun, F.A. Nuzir, B.J. Dewancker, \u201cDynamic land-use map based on twitter data,\u201d Sustainability, vol.9, no.12, p.2158, 2017. 10.3390\/su9122158","DOI":"10.3390\/su9122158"},{"key":"29","doi-asserted-by":"publisher","unstructured":"[29] Y. Jia, Y. Ge, F. Ling, X. Guo, J. Wang, L. Wang, Y. Chen, and X. Li, \u201cUrban land use mapping by combining remote sensing imagery and mobile phone positioning data,\u201d Remote Sensing, vol.10, no.3, p.446, 2018. 10.3390\/rs10030446","DOI":"10.3390\/rs10030446"},{"key":"30","doi-asserted-by":"crossref","unstructured":"[30] J. Yuan, Y. Zheng, and X. Xie, \u201cDiscovering regions of different functions in a city using human mobility and pois,\u201d ACM, p.186, 2012. 10.1145\/2339530.2339561","DOI":"10.1145\/2339530.2339561"},{"key":"31","doi-asserted-by":"publisher","unstructured":"[31] S. Gao, K. Janowicz, and H. Couclelis, \u201cExtracting urban functional regions from points of interest and human activities on location-based social networks,\u201d Transactions in GIS, vol.21, no.3, pp.446-467, 2017. 10.1111\/tgis.12289","DOI":"10.1111\/tgis.12289"},{"key":"32","unstructured":"[32] Y. Chen, J. Li, H. Xiao, X. Jin, S. Yan, and J. Feng, \u201cDual path networks,\u201d arXiv preprint arXiv:1707.01629, 2017."},{"key":"33","doi-asserted-by":"crossref","unstructured":"[33] L. Bottou, \u201cLarge-scale machine learning with stochastic gradient descent,\u201d Proc. COMPSTAT'2010, Physica-Verlag, pp.177-186, 2010.","DOI":"10.1007\/978-3-7908-2604-3_16"},{"key":"34","unstructured":"[34] D. Kingma and J. Ba, \u201cAdam: A method for stochastic optimization,\u201d Computer Science, 2014."},{"key":"35","doi-asserted-by":"crossref","unstructured":"[35] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, \u201cImagenet: A large-scale hierarchical image database,\u201d Proc. IEEE Computer Vision and Pattern Recognition, pp.248-255, 2009. 10.1109\/cvpr.2009.5206848","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"36","unstructured":"[36] A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. 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