1. [1] M. F. Goodchild, "Commentary: whither VGI?," GeoJournal, vol. 72, no. 3-4, pp. 239-244, 2008. [DOI:10.1007/s10708-008-9190-4]

2. [2] F. Terroso-Saenz and A. Munoz, "Land use discovery based on Volunteer Geographic Information classification," Expert Systems with Applications, vol. 140, p. 112892, 2020. [DOI:10.1016/j.eswa.2019.112892]

3. [3] J. Jokar Arsanjani, M. Helbich, M. Bakillah, J. Hagenauer, and A. Zipf, "Toward Mapping Land-Use Patterns From Volunteered Geographic Information," International Journal of Geographical Information Science, vol. 27, no. 12, pp. 2264-2278, 2013. [DOI:10.1080/13658816.2013.800871]

4. [4] X. Liu et al., "Classifying Urban Land Use By Integrating Remote Sensing And Social Media Data," International Journal of Geographical Information Science, vol. 31, no. 8, pp. 1675-1696, 2017. [DOI:10.1080/13658816.2017.1324976]

5. [5] Y. Zheng, L. Capra, O. Wolfson, and H. Yang, "Urban Computing: Concepts, Methodologies, and Applications," ACM Trans. Intell. Syst. Technol., vol. 5, no. 3, pp. 1-55, 2014. [DOI:10.1145/2629592]

6. [6] D. Leung and S. Newsam, "Proximate sensing: Inferring what-is-where from georeferenced photo collections," 2010. [DOI:10.1109/CVPR.2010.5540040]

7. [7] S. Gao, K. Janowicz, and H. Couclelis, "Extracting Urban Functional Regions from Points of Interest and Human Activities on Location‐Based Social Networks," Transactions in GIS, vol. 21, no. 3, pp. 446-467, 2017. [DOI:10.1111/tgis.12289]

8. [8] B. Zhao, Y. Zhong, and L. Zhang, "Hybrid generative/discriminative scene classification strategy based on latent Dirichlet allocation for high spatial resolution remote sensing imagery," in Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International, 2013, pp. 196-199: IEEE. [DOI:10.1109/IGARSS.2013.6721125]

9. [9] T. Pei, S. Sobolevsky, C. Ratti, S.-L. Shaw, T. Li, and C. Zhou, "A new insight into land use classification based on aggregated mobile phone data," International Journal of Geographical Information Science, vol. 28, no. 9, pp. 1988-2007, 2014. [DOI:10.1080/13658816.2014.913794]

10. [10] Y. Liu et al., "Social sensing: A new approach to understanding our socioeconomic environments," Annals of the Association of American Geographers, vol. 105, no. 3, pp. 512-530, 2015. [DOI:10.1080/00045608.2015.1018773]

11. [11] X. Liu et al., "Characterizing Mixed-use Buildings Based on Multi-Source Big data," International Journal of Geographical Information Science, vol. 32, no. 4, pp. 738-756, 2018.

12. [12] Y. Liu, F. Wang, Y. Xiao, and S. Gao, "Urban land uses and traffic 'source-sink areas': Evidence from GPS-enabled taxi data in Shanghai," Landscape and Urban Planning, vol. 106, no. 1, pp. 73-87, 2012. [DOI:10.1016/j.landurbplan.2012.02.012]

13. [13] X. Zhou and L. Zhang, "Crowdsourcing functions of the living city from Twitter and Foursquare data," Cartography and Geographic Information Science, vol. 43, no. 5, pp. 393-404, 2016. [DOI:10.1080/15230406.2015.1128852]

14. [14] A. Sabzali Yameqani and A. Alesheikh, "Developing a Location Distortion Model to Improve Reverse Geocoding with Weather Data," Journal of Geomatics Science and Technology, vol. 9, no. 2, pp. 1-13, 2019.

15. [15] F. Karimipour, M. Tayebi, and K. Amozande, "Characterization of Social Land use in Urban Environments Based on the Semantic Dimension of Location Based Social Networks' Data," (in eng), Journal of Geomatics Science and Technology, Applicable vol. 7, no. 4, pp. 133-145, 2018.

16. [16] S. Jiang, A. Alves, F. Rodrigues, J. Ferreira Jr, and F. C. Pereira, "Mining Point-of-Interest Data from Social Networks for Urban Land use Classification and Disaggregation," Computers, Environment and Urban Systems, vol. 53, pp. 36-46, 2015. [DOI:10.1016/j.compenvurbsys.2014.12.001]

17. [17] H. Hobel, A. Abdalla, P. Fogliaroni, and A. U. Frank, "A Semantic Region Growing Algorithm: Extraction of Urban Settings," in AGILE 2015: Springer, 2015, pp. 19-33. [DOI:10.1007/978-3-319-16787-9_2]

18. [18] R. Andrade, A. Alves, and C. Bento, "POI Mining for Land Use Classification: A Case Study," ISPRS International Journal of Geo-Information, vol. 9, no. 9, p. 493, 2020. [DOI:10.3390/ijgi9090493]

19. [19] S. Hasan, X. Zhan, and S. V. Ukkusuri, "Understanding Urban Human Activity and Mobility Patterns Using Large-scale Location-Based Data From Online Social Media," in Proceedings of the 2nd ACM SIGKDD international workshop on urban computing, New York, 2013, p. 6: ACM, 2013. [DOI:10.1145/2505821.2505823]

20. [20] V. Frias-Martinez, "Spectral Clustering for Sensing Urban Land use Using Twitter Activity," Engineering Applications of Artificial Intelligence, vol. 35, pp. 237-245, 2014. [DOI:10.1016/j.engappai.2014.06.019]

21. [21] L. Wu, X. Cheng, C. Kang, D. Zhu, Z. Huang, and Y. Liu, "A Framework for Mixed-use Decomposition Based on Temporal Activity Signatures Extracted from Big Geo-data," International Journal of Digital Earth, pp. 1-19, 2018. [DOI:10.1080/17538947.2018.1556353]

22. [22] J. L. Toole, M. Ulm, M. C. González, and D. Bauer, "Inferring Land use from Mobile Phone Activity," in Proceedings of the ACM SIGKDD international workshop on urban computing, New York, 2012, pp. 1-8: ACM. [DOI:10.1145/2346496.2346498]

23. [23] M. Jalili, F. Hakimpour, and S. C. Van der Spek, "Extraction of Usage Patterns for Land-Use Types by Pedestrian Trajectory Analysis," in International Symposium on Web and Wireless Geographical Information Systems, Coruña, Spain, 2018, pp. 61-76: Springer. [DOI:10.1007/978-3-319-90053-7_7]

24. [24] Y. Jia et al., "Urban Land Use Mapping by Combining Remote Sensing Imagery and Mobile Phone Positioning Data," Remote Sensing, vol. 10, no. 3, p. 446, 2018. [DOI:10.3390/rs10030446]

25. [25] S. B. Kotsiantis, I. Zaharakis, and P. Pintelas, "Supervised Machine Learning: A Review of Classification Techniques," Emerging artificial intelligence applications in computer engineering, vol. 160, pp. 3-24, 2007. [DOI:10.1007/s10462-007-9052-3]

26. [26] L. Deng and D. Yu, "Deep Learning: Methods and Applications," Foundations and Trends® in Signal Processing, vol. 7, no. 3-4, pp. 197-387, 2014. [DOI:10.1561/2000000039]

27. [27] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT press, 2016.

28. [28] M. Hüsken and P. Stagge, "Recurrent Neural Networks for Time Series Classification," Neurocomputing, vol. 50, pp. 223-235, 2003. [DOI:10.1016/S0925-2312(01)00706-8]

29. [29] A. Graves, A.-r. Mohamed, and G. Hinton, "Speech Recognition with Deep Recurrent Neural Networks," in 2013 IEEE international conference on acoustics, speech and signal processing, Vancouver, 2013, pp. 6645-6649, Vancouver: IEEE. [DOI:10.1109/ICASSP.2013.6638947]

30. [30] P. Liu, X. Qiu, and X. Huang, "Recurrent Neural Network for Text Classification with Multi-task Learning," arXiv preprint arXiv:1605.05101, 2016.

31. [31] A. Graves, "Supervised sequence labelling," in Supervised Sequence Labelling with Recurrent Neural Networks: Springer, 2012, pp. 5-13. [DOI:10.1007/978-3-642-24797-2_2]

32. [32] J. Tanha, Y. Abdi, N. Samadi, N. Razzaghi, and M. Asadpour, "Boosting Methods for Multi-class Imbalanced Data Classification: an Experimental Review," Journal of Big Data, vol. 7, no. 1, p. 70, 2020/09/01 2020. [DOI:10.1186/s40537-020-00349-y]

33. [33] A. P. Association, "Land Based Classification Standards (LBCS)," ed: Retrieved from American Planning Association: http://www. planning. org/lbcs, 2011.