个人简介:
刘康,博士, 中国科学院深圳先进技术研究院副研究员,博士生导师。2018年于中国科学院地理科学与资源研究所(资源与环境信息系统国家重点实验室)取得地理信息科学(GIS)专业博士学位。研究方向为地理空间人工智能(GeoAI)与城市计算。近年来发表学术论文40篇(含12篇一作/通讯高水平SCI论文),申请发明专利20余项(含授权8项)。主持国家自然科学基金(面上、青年项目)、国家重点研发计划子课题、广东省自然科学基金、深圳市自然科学基金和中国博士后科学基金等多项科研项目,曾参与国家自然科学基金重点项目、国家863计划重大项目等国家级项目。2020年被认定为深圳市海外高层次人才,2021年获深圳市科技进步一等奖(排名2/13),2022年获中国自动化学会科技进步二等奖(排名2/10)。
研究方向: 地理空间人工智能(GeoAI)、城市计算
近期关注: 轨迹生成、时空对象表示学习、知识-数据双驱动建模、多模态大模型、时空知识图谱
招生信息: 每年招收1-2名博士/硕士研究生(计算机科学、电子信息、GIS、应用数学等相关专业),从事GeoAI领域前沿研究,发表高水平SCI论文。鼓励参加学术会议、与国内外同行交流。
相关链接: 谷歌学术主页
2020.12 至今 中国科学院深圳先进技术研究院 副研究员 |
2018.07-2020.12 中国科学院深圳先进技术研究院 博士后、助理研究员 |
2012.09 - 2018.06 中国科学院地理科学与资源研究所 资源与环境信息系统国家重点实验室 地理信息科学, 博士 |
2016.10 - 2017.10 美国加州大学圣巴巴拉分校(UCSB) 地理信息科学(GIS) 联合培养 |
2008.09 - 2012.06 中国地质大学(北京) 学士 |
[01] Liu K#, Shi Y#, Wang S, Zhao X, Yin L. Impact of initial outbreak locations on transmission risk of infectious diseases in an intra-urban area[J]. Computational Urban Science, 2024, 4: 23.
[02] Cao Z, Liu K*, Jin X, et al. STAGE: A Spatiotemporal-Knowledge Enhanced Multi-Task Generative Adversarial Network (GAN) for Trajectory Generation[J]. International Journal of Geographical Information Science, 2024.(中科院一区,GIS旗舰期刊)
[03] Luo Y, Cao Z, Jin X, Liu K*, Yin L*. Deciphering Human Mobility: Inferring Semantics of Trajectories with Large Language Models[C]. 2024 25th IEEE International Conference on Mobile Data Management (MDM), Brussels, Belgium, 2024: 289-294.
[04] 刘康. 人类移动数据生成方法:研究进展与趋势探讨[J]. 地球信息科学学报, 2024, 26(4): 831-847.
[05] Liu K*, Jin X, Cheng S, et al. Act2Loc: a synthetic trajectory generation method by combining machine learning and mechanistic models[J]. International Journal of Geographical Information Science, 2023: 1-25. DOI: 10.1080/13658816.2023.2292570.(中科院二区,IF=5.7,GIS旗舰期刊)
[06] He B, Wu X, Liu K* , Yao Y, Chen W, Zhao W. Trends in forest greening and its spatial correlation with bioclimatic and environmental factors in the Greater Mekong Subregion from 2001 to 2020[J]. Remote Sensing, 2022.(中科院二区,IF=5.349)
[07] He B, Hu J, Liu K*, Xue J, Ning L, Fan J. Exploring Park Visit Variability Using Cell Phone Data in Shenzhen, China[J]. Remote Sensing, 2022, 14(3): 499.(中科院二区,IF=5.349 )
[08] He B, Liu K*, Xue Z, et al. Spatial and Temporal Characteristics of Urban Tourism Travel by Taxi—A Case Study of Shenzhen[J]. ISPRS International Journal of Geo-Information, 2021, 10(7): 445.(中科院三区,IF=2.899)
[09] Liu K, Yin L, Zhang M, et al. Facilitating fine-grained intra-urban dengue forecasting by integrating urban environments measured from street-view images [J]. Infectious Diseases of Poverty, 2021, 10: 40.[3].(中科院一区,IF=4.52)
[10] Liu K, Zhang M, Xi G, et al. Enhancing fine-grained intra-urban dengue forecasting by integrating spatial interactions of human movements between urban regions [J]. PLOS Neglected Tropical Diseases, 2020, 14(12): e0008924.[4](中科院一区,IF=4.411)
[11] Liu K, Yin L, Lu F, et al. Visualizing and exploring POI configurations of urban regions on POI-type semantic space[J]. Cities, 2020, 99: 102610.[5](中科院一区,IF=5.835)
[12] Liu K, Qiu P, Gao S, et al. Investigating urban metro stations as cognitive places in cities using points of interest[J]. Cities, 2020, 97: 102561. (中科院一区,IF=5.835)
[13] Liu K, Yin L, Ma Z, et al. Investigating physical encounters of individuals in urban metro systems with large-scale smart card data[J]. Physica A: Statistical Mechanics and its Applications, 2020: 123398.(中科院二区,IF=3.263)
[14] Liu K, Gao S, Lu F. [8]Identifying spatial interaction patterns of vehicle movements on urban road networks by topic modelling[J]. Computers, Environment and Urban Systems, 2019, 74: 50-61.(中科院一区,IF=5.835,IF=5.324)
[15] Liu K, Gao S, Qiu P, et al. Road2vec: Measuring traffic interactions in urban road system from massive travel routes[J]. ISPRS International Journal of Geo-Information, 2017, 6(11): 321.(中科院三区,IF=2.899)
[16] Liu K, Qiu P, Li M, et al. Exploring urban travel routes' characteristics from a geometric perspective[C]//24th International Conference on Geoinformatics. IEEE, 2016: 1-6.(EI)
[17] 刘康, 仇培元, 刘希亮, 等. 利用词向量模型分析城市道路交通空间相关性[J]. 测绘学报, 2017, 46(12):2032-2040.(EI)
[18] 刘康, 段滢滢, 张恒才. 基于路网拓扑层次性表达的驾车路径规划方法[J]. 地球信息科学学报, 2015, 17(9): 1039-1046.(CSCD)
[19] 刘康, 段滢滢, 陆锋. 基于拓扑与形态特征的城市道路交通状态空间自相关分析[J]. 地球信息科学学报, 2014, 16(3): 390-395.(CSCD)
[20] 刘康, 王枫, 翟旭. 全球电离层 TEC 数据统计分析与全局趋势分析[J]. 测绘通报, 2013 (1): 29-32.
[01] Liu K#, Shi Y#, Wang S, Zhao X, Yin L. Impact of initial outbreak locations on transmission risk of infectious diseases in an intra-urban area[J]. Computational Urban Science, 2024, 4: 23.
[02] Qiu P, Pang L, Luo Y*, Liu Y, Xing H, Liu K, Zhuang G. Earthquake Event Knowledge Graph Construction and Reasoning[J]. Geomatics, Natural Hazards and Risk, 2024, 15(1): 2383768.
[03] Cao Z, Liu K*, Jin X, et al. STAGE: A Spatiotemporal-Knowledge Enhanced Multi-Task Generative Adversarial Network (GAN) for Trajectory Generation[J]. International Journal of Geographical Information Science, 2024.
[04] Luo Y, Cao Z, Jin X, Liu K*, Yin L*. Deciphering Human Mobility: Inferring Semantics of Trajectories with Large Language Models[C]. 2024 25th IEEE International Conference on Mobile Data Management (MDM), Brussels, Belgium, 2024: 289-294.
[05] Zhao Y, Cheng S, Liu K, et al. Intercity freight connections in China under the view of mass truck trajectories[J]. Cities, 2024, 150: 105034.
[06] Zhu K, Yin L, Liu K, et al. Generating synthetic population for simulating the spatiotemporal dynamics of epidemics[J]. PLOS Computational Biology, 2024, 20(2): e1011810.
[07] 刘康. 人类移动数据生成方法:研究进展与趋势探讨[J]. 地球信息科学学报, 2024, 26(4): 831-847.
[08] Liu K#*, Jin X#, Cheng S, et al. Act2Loc: a synthetic trajectory generation method by combining machine learning and mechanistic models[J]. International Journal of Geographical Information Science, 2024, 38(3), 407-431. DOI: 10.1080/13658816.2023.2292570.
[09] Jiang J, Xu Z, Zhang Z, Zhang J, Liu K, Kong Hui*. Revealing the fractal and self-similarity of realistic collective human mobility[J]. Physica A: Statistical Mechanics and its Applications, 2023: 129232.
[10] 陈洁, 周莹菲, 尹凌, 李烨, 苗芬, 裴韬, 刘康, 任倩茹, 李璇, 张浩, 李子垠, 奚桂锴. 2020年初COVID-19出院患者日常生活时空行为分析[J]. 地理学报, 2023, 78(4): 1028-1043.
[11] He B, Wu X, Liu K*, Yao Y, Chen W, Zhao W. Trends in forest greening and its spatial correlation with bioclimatic and environmental factors in the Greater Mekong Subregion from 2001 to 2020[J]. Remote Sensing, 2022.
[12] He, B., Huang, D., Kong, B., Liu, K., Zhou, C., Sun, L., & Ning, L. Spatial Variations in Vegetation Greening in 439 Chinese Cities From 2001 to 2020 Based on Moderate Resolution Imaging Spectroradiometer Enhanced Vegetation Index Data[J]. Frontiers in Ecology and Evolution, 2022, 10.
[13] Zhang, H., Yin, L., Mao, L., Mei, S., Chen, T., Liu, K., & Feng, S. Combinational Recommendation of Vaccinations, Mask-Wearing, and Home-Quarantine to Control Influenza in Megacities: An Agent-Based Modeling Study with Large-Scale Trajectory Data[J]. Frontiers in public health, 2022, 10.
[14] He B, Hu J, Liu K*, Xue J, Ning L, Fan J. Exploring Park Visit Variability Using Cell Phone Data in Shenzhen, China[J]. Remote Sensing, 2022, 14(3): 499.
[15] Xi G, Yin L, Liu K. Intra-urban Region-based Traffic Flow Prediction Based on Spatial-Temporal Graph Convolutional Network Enhanced by Spatial Context[C]. The 10th International Workshop on Urban Computing (UrbComp 2021), held in conjunction with the 29th ACM SIGSPATIAL, 2021. Best Paper Award.
[16] 尹凌,刘康,张浩等. 耦合人群移动的COVID-19传染病模型研究进展[J]. 地球信息科学学报, 2021, 23(11): 1894-1909.
[17] 张浩, 尹凌, 刘康等. 深圳市快速抑制COVID-19疫情的非药物干预措施效果评估:基于智能体的建模研究[J]. 地球信息科学学报, 2021, 23(11): 1936-1945.
[18] He B, Liu K*, Xue Z, et al. Spatial and Temporal Characteristics of Urban Tourism Travel by Taxi—A Case Study of Shenzhen[J]. ISPRS International Journal of Geo-Information, 2021, 10(7): 445.
[19] Zeng W, Lin C, Liu K, et al. Modeling Spatial Non-stationarity via Deformable Convolutions for Deep Traffic Flow Prediction[J]. IEEE Transactions on Knowledge and Data Engineering, 2021.
[20] Yin L, Zhang H, Li Y, Liu K, et al. A data driven agent-based model that recommends non-pharmaceutical interventions to suppress Coronavirus disease 2019 resurgence in megacities. Journal of the Royal Society Interface, 2021, 18(181): 20210112.
[21] Li M, Gao S, Lu F, Liu K, er al. Prediction of human activity intensity using the interactions in physical and social spaces through graph convolutional networks [J]. International Journal of Geographical Information Science, 2021, 35:12, 2489-2516.
[22] Liu K, Yin L, Zhang M, et al. Facilitating fine-grained intra-urban dengue forecasting by integrating urban environments measured from street-view images [J]. Infectious Diseases of Poverty, 2021, 10: 40.
[23] Liu K, Zhang M, Xi G, et al. Enhancing fine-grained intra-urban dengue forecasting by integrating spatial interactions of human movements between urban regions [J]. PLOS Neglected Tropical Diseases, 2020, 14(12): e0008924.
[24] Liu K, Yin L, Lu F, et al. Visualizing and exploring POI configurations of urban regions on POI-type semantic space[J]. Cities, 2020, 99: 102610.
[25] Liu K, Qiu P, Gao S, et al. Investigating urban metro stations as cognitive places in cities using points of interest[J]. Cities, 2020, 97: 102561.
[26] Liu K, Yin L, Ma Z, et al. Investigating physical encounters of individuals in urban metro systems with large-scale smart card data[J]. Physica A: Statistical Mechanics and its Applications, 2020: 123398.
[27] Liu K, Gao S, Lu F. Identifying spatial interaction patterns of vehicle movements on urban road networks by topic modelling[J]. Computers, Environment and Urban Systems, 2019, 74: 50-61.
[28] Zhang F, Liu K, Yin L. Investigating Evolutions of Metro Station Functions in Shenzhen with Long-Term Smart Card Data[M]//Geoinformatics in Sustainable Ecosystem and Society. Springer, Singapore, 2019: 33-53.
[29] Wan Q, Yin L, Mao L, Wang L, Mei S, Li Q, Liu K. Simulating Human Host Interventions to Control Intra-urban Dengue Outbreaks with a Spatially Individual-based Model[C]//2019 IEEE International Conference on Real-time Computing and Robotics (RCAR). IEEE, 2019: 537-542.
[30] Lu F, Liu K, Duan Y, et al. Modeling the heterogeneous traffic correlations in urban road systems using traffic-enhanced community detection approach[J]. Physica A: Statistical Mechanics and its Applications, 2018, 501: 227-237.
[31] Liu X, Yu L, Liu K, et al. ST-PF: Spatio-Temporal Particle Filter for Floating Car Data Pre-processing[M]//Wang T, Claramunt C, Thill J.C, et al. Information Fusion and Intelligent Geographic Information Systems (IF&IGIS'17). Cham: Springer, 2018: 197-211.
[32] Liu K, Gao S, Qiu P, et al. Road2vec: Measuring traffic interactions in urban road system from massive travel routes[J]. ISPRS International Journal of Geo-Information, 2017, 6(11): 321.
[33] 刘康, 仇培元, 刘希亮, 等. 利用词向量模型分析城市道路交通空间相关性[J]. 测绘学报, 2017, 46(12):2032-2040.
[34] 李明晓, 陈洁, 张恒才, 仇培元, 刘康, 陆锋. 上海市精细时空尺度人口分布估计与特征分析[J]. 地球信息科学学报, 2017, 19(6): 800-807.
[35] Liu X, Liu K, Li M, et al. SHE: Stepwise Heterogeneous Ensemble Method for Citywide Traffic Analysis[C]//Proceedings of the 1st ACM SIGSPATIAL Workshop on Prediction of Human Mobility. 2017: 1-10.
[36] Liu X, Liu K, Li M, et al. A ST-CRF map-matching method for low-frequency floating car data[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 18(5): 1241-1254.
[37] Liu K, Qiu P, Li M, et al. Exploring urban travel routes' characteristics from a geometric perspective[C]//24th International Conference on Geoinformatics. IEEE, 2016: 1-6.
[38] Liu X, Lu F, Liu K, et al. A Principal Curve-based method for Geospatial Data Smoothing[C]//The 9th International Conference on Geographic Information Science (GIScience 2016), 2016: 352–355.
[39] 刘希亮, 程诗奋, 余丽, 刘康, 陆锋. 架起 GIS 与计算机科学的桥梁: ACM SIGSPATIAL 2015会议综述[J]. 地球信息科学学报, 2016, 18(11): 1448-1455.
[40] 刘康, 段滢滢, 张恒才. 基于路网拓扑层次性表达的驾车路径规划方法[J]. 地球信息科学学报, 2015, 17(9): 1039-1046.
[41] 刘康, 段滢滢, 陆锋. 基于拓扑与形态特征的城市道路交通状态空间自相关分析[J]. 地球信息科学学报, 2014, 16(3): 390-395.
[42] 陆锋, 刘康, 陈洁. 大数据时代的人类移动性研究[J]. 地球信息科学学报, 2014, 16(5): 665-672.
[43] 刘康, 王枫, 翟旭. 全球电离层TEC数据统计分析与全局趋势分析[J]. 测绘通报, 2013 (1): 29-32.
[1] 国家重点研发计划,2023/01-2025/12,参与单位及子课题负责人
[2] 国家自然科学基金(面上项目),2023/01-2026/12,主持
[3] 国家自然科学基金(青年科学基金项目),2020/01-2022/12,主持
[4] 中国博士后科学基金(面上项目),2019/04-2020/04,主持
[1] “地理信息工程国家重点实验室”-“测绘科学与地球空间信息技术自然资源部重点实验室”联合开放基金课题,2024.07—2025.12,主持
[2] 广东省自然科学基金(面上项目),2024/01-2026/12,主持
[3] 广东省自然科学基金(面上项目),2021/01-2023/12,主持
[4] 资源与环境信息系统国家重点实验室开放基金,2019/09-2021/08,主持
[1] 深圳市可持续发展专项项目,2023/11-2026/11,参与单位负责人
[2] 深圳市自然科学基金(面上项目),2020/01-2022/12,主持
[3] 城市空间信息工程北京市重点实验室开放基金,2020/04-2021/03, 主持
[1] 刘康、曹中才,一种时空轨迹生成方法、装置、设备及存储介质,授权日2024.08.16,中国,CN117332033B
[2] 刘康、金鑫,一种轨迹生成方法、装置、设备及存储介质,授权日2024.09.24,中国,CN116822635B
[3] 刘康、尹凌、江锦成,地铁站服务范围确定方法及系统,授权日2022.08.19,中国,CN110895551B
[4] 刘康、尹凌,区域POI配置可视化方法,授权日2022.04.26,中国,CN110909627B
[5] 刘康、尹凌,城市地铁站域的认知场所特征识别方法及系统,授权日2022.05.27,中国,CN110597945B
[6] 尹凌、张帆、刘康,面向地铁乘客的朋友推荐方法及系统,授权日2021.07.20,中国,CN110162520B
[7] 张帆、尹凌、刘康,一种基于刷卡数据的地铁站点功能及其演化识别方法、系统及电子设备,授权日2022.06.21,中国,CN110738244B
[8] 尹凌、张帆、刘康,基于地铁空间的传染病扩散分析方法及系统,授权日2022.07.19,中国,CN110147419B
[9] 刘康、尹凌、奚桂锴,城市内部登革热时空预测方法、系统及电子设备,授权日2024.04.19,中国,CN111554408B
[10] 江锦成、陈劲松、刘康,一种针对城市聚集事件的应急疏散方法与系统,授权日2024.04.23,中国,CN110992233B
[1] 刘康、郑书翔、邓敏、杨学习,地理实体关系抽取方法、装置、计算机设备及存储介质,2024.05.31,中国,CN202410696023.4
[2] 刘康、尹凌、薛建章,初始暴发位置所致传染病时空传播风险定量评估方法,2020.12.09,中国,CN202111503747.5
[3] 刘康、尹凌,城市内部传染病时空扩散建模方法及系统,2020.10.23,中国,CN202011145398.X
[4] 李子垠、尹凌、刘康,城市流感发病趋势预测方法、系统、终端以及存储介质,2021.12.31,中国,CN202111670090.1
[5] 尹凌、陈智、朱克忞、刘康、吕秋莹、吴永胜,传播链构建方法、设备、存储介质和程序产品,2024.10.12,中国,CN202411424930.X
[1] 刘康、曹中才,一种时空轨迹生成方法、装置、计算机设备以及存储介质,2023/12/07,国际,PCTCN2023137233
[2] 刘康、金鑫,一种轨迹生成方法、装置、设备及存储介质,2023.05.18,国际,PCT/CN2023/094915
[3] 刘康、尹凌、薛建章,初始暴发位置所致传染病时空传播风险定量评估方法,2022/12/08,国际,PCT/CN2022/137490
[4] 刘康、尹凌,城市内部传染病时空扩散建模方法及系统,2020.12.25,国际,PCT/CN2020/139304
[5] 刘康、尹凌、奚桂锴,城市内部登革热时空预测方法、系统及电子设备,2020.12.25,国际,PCT/CN2020/139657
[6] 李子垠、尹凌、刘康,城市流感发病趋势预测方法、系统、终端以及存储介质,2022.02.15,国际,PCT/CN2022/076294
[1] 城市区域POI配置可视分析软件V1.0,著作权人:中国科学院深圳先进技术研究院,开发完成日期2019年11月04日,登记号:2024SR0147235,证书号:软著登字第12551108号,登记日:2024年01月23日,中华人民共和国国家版权局
[2] 地铁站服务范围可视化系统V1.0,著作权人:中国科学院深圳先进技术研究院,开发完成日期2019年12月10日,登记号:2024SR0239282,证书号:软著登字第12643155号,登记日:2024年02月06日,中华人民共和国国家版权局
[01] 基于大语言模型的个体时空轨迹语义解析,第十九届地理信息科学理论与方法学术年会,2024.10,西安,中国
[02] 机理-数据双驱动的人类时空轨迹生成方法及应用,第五届中国空间数据智能学术会议(SpatialDI 2024),2024.04,南京,中国
[03] 数据-机理双驱动的个体移动行为过程模拟方法,中国行为地理学年会,2023.12,上海,中国
[04] Act2Loc: A Trajectory Generation Method by Combining Machine Learning and Mechanism Model, Urban Informatics, 2023.08, Hong Kong, China
[05] Generating Synthetic Trajectories by Combining Machine Learning and Mechanism Model, Geoinformatics, 2023.08, London, UK
[06] 耦合机理模型与机器学习的海量个体轨迹生成方法,中国地理信息科学理论与方法学术年会,2023.05,桂林,中国
[07] 城市内部传染病初始暴发位置对时空传播风险的影响研究,中国地理信息科学理论与方法学术年会,2021,杭州,中国
[08] 城市区域POI配置可视探索方法,中国地理信息科学理论与方法学术年会,2019,上海,中国
[09] Investigating urban metro stations as cognitive places in cities using points of interest, Location Big Data, 2019, Tokyo, Japan
[10] Exploring urban metro stations as cognitive places in cities using points of interest, Urban Informatics, 2019, Hong Kong, China
[11] 城市道路网络视角下的空间交互模式识别方法,中国地理信息科学理论与方法学术年会,2018,太原,中国
[12] Quantifying urban road traffic interactions from massive travel routes using word embedding techniques, AAG Annual Meeting, 2017, Boston, United States
[13] Identifying City Travel Routes’ Characteristics from a Geometric Perspective, Geoinformatics, 2016, Galway, Ireland
[14] 考虑道路拓扑结构与交通状态的出行路径模拟方法,全国地理信息科学博士生学术论坛,2014,南京,中国
[15] 城市路网交通状态数据探索性分析,中国地理信息科学理论与方法学术年会,2013,福州,中国
[1] 《测绘学报》首届青年编委(EI收录,中国测绘地理信息科学领域具有重要影响力的学术期刊)
[2] 国家自然科学基金委员(地球一处)基金评审专家
[3] 广东省科技厅、深圳市科创委、罗湖区科创局等项目评审专家
[4] 领域主流期刊IJGIS、TGIS、CEUS、EPB、IEEE TITS、Cities、Physica A、测绘学报、地理学报、地球信息科学学报、地理科学、地理科学进展等审稿人
[5] 中国测绘学会智能化测绘工作委员会委员、中国地理学会会员、中国计算机学会会员、中国自动化学会会员、ACM中国SIGSPATIAL分会会员
[6] 深圳市城市公共安全技术研究院企业联合培养博士后导师、中国矿业大学硕士研究生校外合作导师
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[4] 中国仪器仪表学会科技进步三等奖(排名5/16)(2021),灾害事故下辅助决策关键技术研究及应用,袁狄平,尹继尧,杨丽丽,吴国华,刘康,杨双华,张鹏辉,曾益萍,邓鹏,江锦成,汪作新丁宇龙,周琳,陈文刚,张艳辉,姜洁
[5] 深圳市海外高层次人才-孔雀计划C类(2020)
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