个人简历
李圣文，男，软件工程系主任，副教授，博士，硕士生导师，中国计算机学会(CCF)会员，AAG会员。2000年7月-至今，任教于中国地质大学（武汉）计算机学院软件工程系；2015年1月-2016年1月，赴KSU访问学习一年。近年来主持和参与国家项目8项，其中主持国家自然科学基金面上项目2项，任SCI期刊Computational Urban Science编委；出版专著1部，教材3部；指导学生在全国高校互联网应用创新大赛等获奖多项。
研究方向
[1] 机器学习（自然语言处理方向）
[2] 知识图谱
[3] 时空大数据挖掘
主讲课程
近年主要主讲本科生《Java与.Net软件开发》、《Web软件开发》、《计算机网络》、《软件测试》、《面向对象软件工程》、《软件工程专业前沿文献》及研究生《高级程序设计》、《知识图谱》等课程及相关实习实践课程。
近年科研论文
[1] Zhou, S.; Feng, Y.; Li, S.; Zheng, D.; Fang, F.; Liu, Y.; Wan, B. DSM-Assisted Unsupervised Domain Adaptive Network for Semantic Segmentation of Remote Sensing Imagery. IEEE Trans. Geosci. Remote Sensing 2023, 61, 1–16.
[2] Zheng, D.; Li, S.; Fang, F.; Zhang, J.; Feng, Y.; Wan, B.; Liu, Y. Utilizing Bounding Box Annotations for Weakly Supervised Building Extraction From Remote-Sensing Images. IEEE Trans. Geosci. Remote Sensing 2023, 61, 1–17.
[3] Zheng, D.; Kang, J.; Wu, K.; Feng, Y.; Guo, H.; Zheng, X.; Li, S.; Fang, F. Semi-Supervised Building Detection from High-Resolution Remote Sensing Imagery. Sustainability (Switzerland) 2023, 15, 11789.
[4] Ye, Y.; Xiao, Y.; Zhou, Y.; Li, S.; Zang, Y.; Zhang, Y. Dynamic multi-graph neural network for traffic flow prediction incorporating traffic accidents. Expert Systems with Applications 2023, 234, 121101.
[5] Wan, B.; Dong, S.; Chu, D.; Li, H.; Liu, Y.; Fu, J.; Fang, F.; Li, S.; Zhou, D. A deep neural network model for coreference resolution in geological domain. Information Processing & Management 2023, 60, 103268.
[6] Li, S.; Yang, W.; Huang, S.; Chen, R.; Cheng, X.; Zhou, S.; Gong, J.; Qian, H.; Fang, F. A hierarchical constraint-based graph neural network for imputing urban area data. International Journal of Geographical Information Science 2023, 1–22.
[7] Fang, F.; Xu, R.; Li, S.; Hao, Q.; Zheng, K.; Wu, K.; Wan, B. Semi-supervised building instance extraction from high-resolution remote sensing imagery. IEEE Trans. Geosci. Remote Sensing 2023, 1–1.
[8] Chen, Q.; Yao, H.; Zhou, D.; Li, S.; Dong, L. Extracting fact-condition relation from geological papers via deep structured semantic model with multi-grained representation. Computers & Geosciences 2023, 178, 105416.
[9] Chen, Q.; Yao, H.; Li, S.; Li, X.; Kang, X.; Lai, W.; Kuang, J. Fact-condition statements and super relation extraction for geothermic knowledge graphs construction. Geoscience Frontiers 2023, 14, 101412.
[10] Zhang, X.; Zheng, Y.; Ye, X.; Peng, Q.; Wang, W.; Li, S. Clustering with implicit constraints: A novel approach to housing market segmentation. Transactions in GIS 2022, 26, 585–608.
[11] Li, S.; Sun, C.; Chen, R.; Li, X.; Liang, Q.; Gong, J.; Yao, H. Location-aware neural graph collaborative filtering. International Journal of Geographical Information Science 2022, 36, 1550–1574.
[12] Li, S.; Chen, R.; Sun, C.; Yao, H.; Cheng, X.; Li, Z.; Li, T.; Kang, X. Region-aware neural graph collaborative filtering for personalized recommendation. International Journal of Digital Earth 2022, 15, 1446–1462.
[13] Fang, F.; Zheng, D.; Li, S.; Liu, Y.; Zeng, L.; Zhang, J.; Wan, B. Improved Pseudomasks Generation for Weakly Supervised Building Extraction from High-Resolution Remote Sensing Imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2022, 15, 1629–1642.
[14] Fang, F.; Zeng, L.; Li, S.; Zheng, D.; Zhang, J.; Liu, Y.; Wan, B. Spatial context-aware method for urban land use classification using street view images. ISPRS Journal of Photogrammetry and Remote Sensing 2022, 192, 1–12.
[15] Zhang, X.; Zhang, Z.-K.; Wang, W.; Hou, D.; Xu, J.; Ye, X.; Li, S. Multiplex network reconstruction for the coupled spatial diffusion of infodemic and pandemic of COVID-19. International Journal of Digital Earth 2021, 14, 401–423.
[16] Ye, X.; Li, S.; Peng, Q. Measuring interaction among cities in China: A geographical awareness approach with social media data. Cities 2021, 109, 103041.
[17] Ye, X.; Gong, J.; Li, S. Analyzing Asymmetric City Connectivity by Toponym on Social Media in China. Chin. Geogr. Sci. 2021, 31, 14–26.
[18] Li, S.; Li, B.; Yao, H.; Zhou, S.; Zhu, J.; Zeng, Z. Completing WordNets with Sememe Knowledge. Electronics 2021, 11, 79.
[19] Lee, J.; Li, S.; Wang, S.; Wang, J.; Li, J. Spatio-Temporal Nearest Neighbor Index for Measuring Space-Time Clustering among Geographic Events. Papers in Applied Geography 2021, 7, 117–130.
[20] Gong, J.; Li, S.; Ye, X.; Peng, Q.; Kudva, S. Modelling impacts of high-speed rail on urban interaction with social media in China’s mainland. Geo-spatial Information Science 2021, 24, 638–653.
[21] Fang, F.; Yu, Y.; Li, S.; Zuo, Z.; Liu, Y.; Wan, B.; Luo, Z. Synthesizing location semantics from street view images to improve urban land-use classification. International Journal of Geographical Information Science 2021, 35, 1802–1825.
[22] Fang, F.; Wu, K.; Liu, Y.; Li, S.; Wan, B.; Chen, Y.; Zheng, D. A Coarse-to-Fine Contour Optimization Network for Extracting Building Instances from High-Resolution Remote Sensing Imagery. Remote Sensing 2021, 13, 3814.
[23] Dong, L.; Yao, H.; Li, D.; Wang, Y.; Li, S.; Liang, Q. Improving graph neural network via complex-network-based anchor structure. Knowledge-Based Systems 2021, 233, 107528.
[24] Zhen, W.; Yang, L.; Kwan, M.-P.; Zuo, Z.; Wan, B.; Zhou, S.; Li, S.; Ye, Y.; Qian, H.; Pan, X. Capturing what human eyes perceive: A visual hierarchy generation approach to emulating saliency-based visual attention for grid-like urban street networks. Computers, Environment and Urban Systems 2020, 80, 101454.
[25] Zhang, D.; Zhang, X.; Zheng, Y.; Ye, X.; Li, S.; Dai, Q. Detecting Intra-Urban Housing Market Spillover through a Spatial Markov Chain Model. IJGI 2020, 9, 56.
[26] Yu, Y.; Fang, F.; Liu, Y.; Li, S.; Luo, Z. Urban Land Use Classification Using Street View Images Based on Deep Transfer Network. In Urban Intelligence and Applications; 2020; pp. 83–95.
[27] Li, S.; Chen, R.; Wan, B.; Gong, J.; Yang, L.; Yao, H. DAWE: A Double Attention-Based Word Embedding Model with Sememe Structure Information. Applied Sciences 2020, 10, 5804.
[28] Kang, X.; Li, B.; Yao, H.; Liang, Q.; Li, S.; Gong, J.; Li, X. Incorporating Synonym for Lexical Sememe Prediction: An Attention-Based Model. Applied Sciences 2020, 10, 5996.
[29] Gong, J.; Lee, J.; Zhou, S.; Li, S. Toward Measuring the Level of Spatiotemporal Clustering of Multi-Categorical Geographic Events. IJGI 2020, 9, 440.
[30] Chen, R.; Yao, H.; Li, R.; Kang, X.; Li, S.; Dong, L.; Gong, J. Identifying Human Daily Activity Types with Time-Aware Interactions. Applied Sciences 2020, 10, 8922.
[31] Jia, H.; Yao, H.; Tian, T.; Yan, C.; Li, S. The latent semantic power of labels: Improving image classification via natural language semantic. In Proceedings of the Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 2019; Vol. 11956 LNCS, pp. 175–189.
[32] Gong, J.; Li, S.; Wan, B. A Regional Approach to Assessing and Visualizing Spatiotemporal Clustering of Crime Events. Papers in Applied Geography 2019, 5, 26–44.
[33] Gong, J.; Li, R.; Yao, H.; Kang, X.; Li, S. Recognizing human daily activity using social media sensors and deep learning. International Journal of Environmental Research and Public Health 2019, 16, 3955.
[34] Ye, X.; Li, S.; Sharag-Eldin, A.; Tsou, M.-H.; Spitzberg, B. Geography of Social Media in Public Response to Social Topics. In Geospatial Data Science Technologies and Applications; 2018; pp. 167–179.
[35] Gong, J.; Li, S.; Lee, J. Space, time, and disease on social media: a case study of dengue fever in China. Geomatica 2018, 72, 112–126.
[36] Li, S.; Ye, X.; Lee, J.; Gong, J.; Qin, C. Spatiotemporal Analysis of Housing Prices in China: A Big Data Perspective. Applied Spatial Analysis and Policy 2017, 10, 421–433.
[37] Lee, J.; Li, S. Extending Moran’s Index for Measuring Spatiotemporal Clustering of Geographic Events: Extending Moran’s Index. Geogr Anal 2017, 49, 36–57.
[38] Lee, J.; Gong, J.; Li, S. Exploring spatiotemporal clusters based on extended kernel estimation methods. International Journal of Geographical Information Science 2017, 31, 1154–1177.
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