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Dr Bingnan Li

Dr Bingnan Li

Research Associate

Ph.D. (Civil and Environmental Engineering), University of New South Wales, Sydney, NSW, Australia.

M.A. (Cartography and Geographic Information System) East China Normal University, Shanghai, China.

B.A. (Geographic Information System) East China Normal University, Shanghai, China.

Medicine & Health
The Kirby Institute

Dr. Bingnan Li is a Research Associate (research-focused academic staff) in the Biosecurity Program of Kirby Institute, Faculty of Medicine and Health, UNSW Sydney. He holds a Ph.D. degree in Geospatial Data Mining from the School of Civil and Environmental Engineering, Faculty of Engineering at UNSW, and a Master degree in Geographical Information Systems (GIS) from East China Normal University, Shanghai, China. Under the supervision of A/Prof. Samsung Lim, his Ph.D. research is focused on deep learning-based spatio-temporal data mining using multi-source geospatial data. He proposed four efficient approaches in different scenarios for spatio-temporal data mining that take advantage of multi-source geospatial data to overcome the limitations of traditional data mining methods. He also applies GIS to real-world problems and helps decision-making, including red-flagging of epidemics. His research fields include geospatial analysis, geospatial data mining, and social media analysis of infectious diseases.
Phone
+61 2 9348 1486
E-mail
bingnan.li@unsw.edu.au
Location
Level 6, Wallace Wurth Building UNSW SYDNEY 2052
  • Book Chapters | 2021
    Chen Z; Pokharel B; Li B; Lim S, 2021, 'Location Extraction from Twitter Messages Using a Bidirectional Long Short-Term Memory Neural Network with Conditional Random Field Model', in Geographical Information Systems Theory, Applications and Management, pp. 18 - 30, http://dx.doi.org/10.1007/978-3-030-76374-9_2
    Book Chapters | 2021
    Li B; Chen Z; Lim S, 2021, 'Geolocation Inference Using Twitter Data: A Case Study of COVID-19 in the Contiguous United States', in Geographical Information Systems Theory, Applications and Management, pp. 119 - 139, http://dx.doi.org/10.1007/978-3-030-76374-9_8
  • Journal articles | 2024
    Li B; Gao J; Chen S; Lim S; Jiang H, 2024, 'DF-DRUNet: A decoder fusion model for automatic road extraction leveraging remote sensing images and GPS trajectory data', International Journal of Applied Earth Observation and Geoinformation, 127, http://dx.doi.org/10.1016/j.jag.2023.103632
    Journal articles | 2022
    Li B; Gao J; Chen S; Lim S; Jiang H, 2022, 'POI Detection of High-Rise Buildings Using Remote Sensing Images: A Semantic Segmentation Method Based on Multitask Attention Res-U-Net', IEEE Transactions on Geoscience and Remote Sensing, 60, http://dx.doi.org/10.1109/TGRS.2022.3174399
  • Conference Papers | 2022
    Li B; Chen L; Xiong D; Chen S; He R; Sun Z; Lim S; Jiang H, 2022, 'Simultaneous detection of multiple areas-of-interest using geospatial data from an online food delivery platform (industrial paper)', in GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems, http://dx.doi.org/10.1145/3557915.3561014
    Conference Papers | 2020
    Li B; Chen Z; Lim S, 2020, 'Geolocation prediction from tweets: A case study of influenza-like illness in Australia', in Grueau C; Laurini R; Ragia L (eds.), GISTAM 2020 - Proceedings of the 6th International Conference on Geographical Information Systems Theory, Applications and Management, INSTICC, ELECTR NETWORK, pp. 160 - 167, presented at Proceedings of the 6th International Conference on Geographical Information Systems Theory, Applications and Management, ELECTR NETWORK, 07 May 2020 - 09 May 2020, http://dx.doi.org/10.5220/0009345101600167
    Conference Papers | 2020
    2020, 'Location extraction from twitter messages using bidirectional long short-term memory model', http://dx.doi.org/10.5220/0009338800450050