团队主要方向：

[1]     时空大数据组织与计算

大规模分布式空间数据组织与云服务、高性能地理计算、时空大数据智能服务等，支撑新一代人工智能GIS软件-国产时空大数据智能服务平台研制。团队骨干成员包括美国哈佛大学地理分析中心留学归国青年老师、日本名古屋大学海归博士等。

[2]     地理人工智能与自动驾驶

地理人工智能深度学习方法、车辆出行大数据与遥感大数据AI分析、大规模遥感深度学习样本库管理系统与服务、众包感知高精地图等，助力无人机航拍AI违建监测、耕地动态监测监管、出行大数据POI推荐、出行订单需求预测、自动驾驶等。团队骨干成员包括全国博士后创新人才支持计划入选者、瑞典皇家理工学院海归博士等。

[3]     智慧城市、自然资源与灾害应急监测等

通过多个校企平台，包括湖北省空间信息智能处理工程技术研究中心、武汉大学-长安汽车空间信息技术联合实验室、武汉大学-东风集团大数据AI联合实验室等，开展产学研应用研究。相关成果已在智慧城市、国土三调、电网信息化、不动产登记、时空信息云平台、应急管理、防灾减灾等领域推广应用。团队骨干成员包括美国UCSB空间认知中心留学归国青年老师、湖北省楚天学子等。

[4]     国际技术合作与研发

基于多源对地观测时空立方体开展的OGC Testbed创新计划项目系列、面向应急空间信息服务的OGC Pilot减灾先导研发计划系列、OGC首个地理人工智能标准研制等。团队骨干成员包括美国GMU留学归国青年老师等。

团队已形成专利、软著受理或授权五十余项，项目来自国家自然科学基金、国家重点研发计划、国际OGC、湖北省重点研发计划、国家及省市人才计划、产学研合作资助等。

主要荣誉与奖励：

[1]     2023 OGC Community Impact Award

[2]     2022年中国测绘科技进步特等奖（排名1）

[3]     2021全国鲲鹏应用创新大赛广东赛区政府赛道冠军

[4]     2020年华为云“东风出行杯”大数据开发者大赛冠军

[5]     2017国际开放建模协会OpenMI Award

[6]     2016年湖北省自然科学一等奖 (排名1)

[7]     国家优秀青年科学基金

[8]     教育部长江青年学者

[9]     国家万人计划“青年拔尖人才”

[10]   2014年教育部自然科学一等奖（排名2）

[11]   教育部“新世纪优秀人才”

[12]   湖北省杰出青年基金

[13]   武汉市“黄鹤英才”

[14]   武汉大学计算机学科ESI贡献一等奖

[15]   武汉大学“珞珈特聘教授”

[16]   2013年欧洲空间信息基础设施欧盟挑战赛“水晶牛奖”（First Place Award）

[17]   2013年湖北省自然科学一等奖 (排名4)

[18]   2006 IEEE国际服务计算大赛3^rd place award

[19]   2006年全球语义网挑战竞赛Honorable Mention Award

[20]   2004 International Carbon Cup Open Geospatial Design Competition冠军

主要学术兼职：

[1]     ISO/TC211 WG 4 Convenor国际标准化组织地理信息技术委员会第四工作组（地理信息服务工作组）召集人

[2]     OGC地理人工智能样本语言标准工作组 主席（2021至今）

[3]     武汉大学学报 信息科学版副主编（GIS方向）（2021至今）

[4]     中国测绘学会第二届无人机创新工作委员会 副主任委员

[5]     国际开放地理信息协会OGC中国联盟 创会主席（2017至今）

[6]     IEEE地球科学与遥感协会GRSS 地球信息科学（ESI）技术委员会主席（2015-2017）

[7]     OGC环境数据获取API标准工作组 联合主席（2020至今）

[8]     ISO/TC 211国际标准化组织代表（2019至今）

[9]     ISDE国际数字地球学会中国国家委员会委员（2017至今）

[10]   ISPRS国际摄影测量与遥感协会第六委员会 秘书（2012-2016）

[11]   ISPRS国际摄影测量与遥感协会虚拟地球工作组 秘书（2008-2012）

[12]   第六、七届国务院学位委员会测绘科学与技术学科评议组 秘书 （2009-2020）

[13]   中国GIS理论与方法委员会委员（2017至今）

[14]   中国大百科全书测绘学地理信息工程编委 (2015至今)

[15]   中国测绘学名词审定工作委员会委员 (2014至今)

[16]   《遥感学报》编委 (2020至今)

[17]   《地理与地理信息科学》编委(2016至今)

[18]   国际开放地理信息协会（OGC）“新空间”中国研讨会 会议主席（2021）

[19]   2018 IEEE国际地理空间大数据与数据科学会议 大会主席

[20]   2016、2017年国际农业地理信息科学与工程大会科学委员会 联合主席

[21]   2016年国际摄影测量与遥感协会大会（ISPRS Congress） 程序委员会委员

[22]   2015、2016、2017、2018、2019年IEEE国际地球科学与遥感大会 科学委员会委员或分会主席

主要论著：

[1]     Yuan, M., Yue, P., Yang, C., Li, J, Yan, K., Cai, C., & Wan, C., 2023. Generating lane-level road networks from high-precision trajectory data with lane-changing behavior analysis, International Journal of Geographical Information Science, DOI: 10.1080/13658816.2023.2279977

[2]     Cao, Z., Jiang, L., Yue, P., Gong, J., Hu, X., Liu, S., Tan, H., Liu, C., Shangguan, B., & Yu, D., 2023. A large scale training sample database system for intelligent interpretation of remote sensing imagery, Geo-spatial Information Science, DOI: 10.1080/10095020.2023.2244005

[3]     Zhang, M., Yue, P., Hu, L., Wu, H., Zhang, F., 2023. An interoperable and service-oriented approach for real-time environmental simulation by coupling OGC WPS and SensorThings API, Environmental Modelling & Software, 165(7), 105722.

[4]     Fang, Z., Yue, P., Zhang, M., Xie, J., Wu, D., Jiang, L., 2023. A service-oriented collaborative approach to disaster decision support by integrating geospatial resources and task chain, International Journal of Applied Earth Observations and Geoinformation, 117, 103217.

[5]     Xu, M., Yue, P., Yu, F., Yang, C., Zhang, M., Li, S., and Li, H., 2023. Multi-agent reinforcement learning to unify order-matching and vehicle-repositioning in ride-hailing services, International Journal of Geographical Information Science, 37(2), pp. 380-402

[6]     Wang, M., Yu, D., He, W., Yue, P., Liang, Z., 2023. Domain-incremental learning for fire detection in space-air-ground integrated observation network, International Journal of Applied Earth Observations and Geoinformation, 118(4), 103279.

[7]     Yu, D., Yue, P., Ye, F., Tapete, D., Liang, Z., 2023. Bidirectionally greedy framework for unsupervised 3D building extraction from airborne-based 3D meshes, Automation in Construction, 152(8), 104917.

[8]     Li, H., Yue, P., Li, S., Zhang, C., Yang, C., 2023. Spatio-temporal intention learning for recommendation of next point-of-interest, Geo-spatial Information Science, DOI: 10.1080/10095020.2023.2179428

[9]     Gao, F., Yue, P., Cao, Z., Zhao, S., Shangguan, B., Jiang, L., Hu, L., Fang, Z., Liang, Z., 2022. A multi-source spatio-temporal data cube for large-scale geospatial analysis, International Journal of Geographical Information Science, DOI: 10.1080/13658816.2022.2087222

[10]   Yue, P., Shangguan, B., Hu, L., Jiang, L., Zhang, C., Cao, Z., Pan, Y., 2022. Towards a training data model for artificial intelligence in earth observation. International Journal of Geographical Information Science, DOI: 10.1080/13658816.2022.2087223

[11]   Zhang, C., Feng, Y., Hu, L., Tapete, D., Pan, L., Liang, Z., Cigna, F., Yue, P., 2022. A domain adaptation neural network for change detection with heterogeneous optical and SAR remote sensing images. International Journal of Applied Earth Observation and Geoinformation, 109(5), 102769

[12]   Zhang, M., Jiang, L., Yue, P., and Gong, J., 2020. Interoperable web sharing of environmental models using OGC web processing service and Open Modeling Interface (OpenMI). Environmental Modelling & Software, 133: 104838.

[13]   Yue, P., Gao, F., Shangguan, B., and Yan, Z., 2020. A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing. International Journal of Geographical Information Science, 34(11), 2243-2274.

[14]   Zhang, M., Jiang, L., Zhao, J., Yue, P., and Zhang, X., 2020. Coupling OGC WPS and W3C PROV for provenance-aware geoprocessing workflows. Computers and Geosciences, 138: 104419. 

[15]   Zhang, C., Yue, P., Tapete, D., Jiang, L., Shangguan, B., Huang, L., and Liu, G., 2020. A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images. ISPRS Journal of Photogrammetry and Remote Sensing, 166, 183-200.

[16]   Zhang, C., Yue, P., Tapete, D., Shangguan, B., Wang, M., and Wu, Z., 2020. A multi-level context-guided classification method with object-based convolutional neural network for land cover classification using very high resolution remote sensing images. International Journal of Applied Earth Observation and Geoinformation, 88, 102086. DOI: https://doi.org/10.1016/j.jag.2020.102086

[17]   Zhang, X., Zhang, M., Jiang, L., and Yue, P., 2020. An interactive 4D spatio-temporal visualization system for hydrometeorological data in natural disasters. International Journal of Digital Earth, 13(11), pp. 1258-1278.

[18]   Zhang, X., Yue, P., Chen, Y., and Hu, L., 2019. An efficient dynamic volume rendering for large-scale meteorological data in a virtual globe. Computers and Geosciences, 126, pp. 1-8.

[19]   Shangguan, B., Yue, P., Yan, Z., and Tapete, D., 2019. A stream computing approach for live environmental models using a spatial data infrastructure with a waterlogging model case study. Environmental Modelling & Software, 119: 182-196.

[20]   Gao, F., Yue, P., Zhang, C., and Wang, M., 2019. Coupling components and services for integrated environmental modelling. Environmental Modelling & Software, 118: 14-22.

[21]   Jiang, L., Yue, P., Kuhn, W., Zhang, C., Yu, C., and Guo, X., 2018. Advancing interoperability of geospatial data provenance on the web: Gap analysis and strategies. Computers & Geosciences, 117(8): 21-31.

[22]   Sun, Z., Di, L., Hao, H., Wu, X., Tong, D.Q., Zhang, C., Virgei, C., Fang, H., Yu, E., Tan, X. Yue, P., and Lin, L., 2018. CyberConnector: a service-oriented system for automatically tailoring multisource Earth observation data to feed Earth science models. Earth Science Informatics, 11(1): 1-17.

[23]   Zhang, M., Yue, P., Wu, Z., Ziebelin, D., Wu, H. and Zhang, C., 2017. Model provenance tracking and inference for integrated environmental modelling. Environmental Modelling & Software, 96: 95-105.

[24]   Tan, Z., Yue, P. and Gong, J., 2017. An Array Database Approach for Earth Observation Data Management and Processing. ISPRS International Journal of Geo-Information, 6(7), 220.

[25]   Gong, J., Yue, P., Woldai, T., Tsai, F., Vyas, A., Wu, H., Gruen, A., Wang, L., and Musikhin, I., 2017. Geoinformatics education and outreach: looking forward. Geo-spatial Information Science, 20(2): 209-217.

[26]   Zhang, M., Bu, X. and Yue, P., 2017. GeoJModelBuilder: an open source geoprocessing workflow tool. Open Geospatial Data, Software and Standards, 2, 1-8.

[27]   Sun, Z., Di, L., Heo, G., Zhang, C., Fang, H., Yue, P., Jiang, L., Tan, X., Guo, L., & Lin, L., 2017. GeoFairy: Towards a one-stop and location based Service for Geospatial Information Retrieval. Computers, Environment and Urban Systems, 62, 156-167.

[28]   Yue, P., Ramachandran, R., Baumann, P., Khalsa, S., Deng, M., and Jiang, L., 2016. Recent Activities in Earth Data Science. IEEE Geoscience and Remote Sensing Magazine, 4(4): 84-89.

[29]   Li, M., Zhu, X., Guo, W., Yue, P., and Fan, Y., 2016. A case-based reasoning approach for task-driven remote sensing image discovery under spatial–temporal constraints. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(1), 454-466.

[30]   Sun, Z., Fang, H., Di, L., Yue, P., Tan, X., and Bai, Y., 2016. Developing a web-based system for supervised classification of remote sensing images. GeoInformatica. 20(4), 629-649.

[31]   Sun, Z., Fang, H., Di, L., and Yue, P., 2016. Realizing parameterless automatic classification of remote sensing imagery using ontology engineering and cyberinfrastructure techniques, Computers & Geosciences, 94: 56-67.

[32]   Yue, P., Guo, X., Zhang, M., Jiang, L., and Zhai, X., 2016. Linked data and SDI: the case on web geoprocessing workflows. ISPRS Journal of Photogrammetry and Remote Sensing. 114: 245-257.

[33]   Yue, P., Zhang, C., Zhang, M., Zhai, X., and Jiang, L., 2015. An SDI approach for big data analytics: the case on sensor web event detection and geoprocessing workflow. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(10), 4720- 4728.

[34]   Yue, P., Ramachandran, R., Baumann, P., 2015. Editorial: intelligent GIServices. Earth Science Informatics, 8(3), 461-462.

[35]   Yue, P., Baumann, P., Bugbee, K., and Jiang, L., 2015. Towards Intelligent GIServices. Earth Science Informatics, 8(3), 463-481.

[36]   Yue, P., Zhang, M., and Tan, Z., 2015. A geoprocessing workflow system for environmental monitoring and integrated modelling. Environmental Modelling & Software. 69, 128-140.

[37]   Hu, L., Yue, P., Zhang, M., Gong, J., Jiang, L., Zhang, X., 2015. Task-oriented Sensor Web data processing for environmental monitoring. Earth Science Informatics, 8(3), 511-525.

[38]   Sun, Z., Fang, H., Deng, M., Chen, A., Yue, P., and Di, L., 2015. Regular Shape Similarity Index: A Novel Index for Accurate Extraction of Regular Objects from Remote Sensing Images. IEEE Transactions on Geoscience and Remote Sensing, 53(7), 3737-3748.

[39]   He, L., Yue, P., Jiang, L., and Zhang, M., 2015. Fuzzy spatial relation ontology driven detection of complex geospatial features in a web service environment. Earth Science Informatics, 8(1), 63-76.

[40]   He, L., Yue, P., Di, L., Zhang, M., and Hu, L., 2015. Adding geospatial data provenance into SDI - a service-oriented approach. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(2), 926-936.

[41]   Sun, Z., Peng, C., Deng, M., Chen, A., Yue, P., Fang, H., and Di, L., 2014. Automation of customized and near-real-time vegetation condition index generation through cyberinfrastructure-based geoprocessing workflows. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(11), 4512-4522.

[42]   Zhai, X, Yue, P., Jiang, L., Wang, L., 2014. Three-dimensional geospatial information service based on cloud computing, Journal of Applied Remote Sensing, 8(1): 085195.

[43]   Yue, P., Jiang, L., Hu, L., 2014. Google Fusion Tables for managing soil moisture sensor observations. IEEE Journal of Selected Topics in Applied Earth Observations And Remote Sensing, 7(11), 4414-4421.

[44]   Li, D., Gong, J., and Yue, P., 2014. Geoinformatics education in China. Geo-spatial Information Science, 17(4), 208-218.

[45]   Han, W., Yang, Z., Di, L., and Yue, P., 2014. A Geospatial Web Service Approach for Creating On-demand Cropland Data Layer Thematic Maps. Transactions of the ASABE, 57(1), 239-247.

[46]   Yuan, J., Yue, P., Gong, J., and Zhang, M., 2013. A linked data approach for geospatial data provenance. IEEE Transactions on Geoscience and Remote Sensing, 51(11), 5105-5112.

[47]   Yue, P., Di, L., Wei, Y., and Han, W., 2013. Intelligent services for discovery of complex geospatial features from remote sensing imagery. ISPRS Journal of Photogrammetry and Remote Sensing. 83: 151-164.

[48]   Sun, Z., Yue, P., Hu, L., Gong, J., Zhang, L., Lu, X., 2013. GeoPWProv: interleaving map and faceted metadata for provenance visualization and navigation. IEEE Transactions on Geoscience and Remote Sensing, 51(11), 5131-5136.

[49]   Yue, P., Zhou, H., Gong, J., and Hu, L., 2013. Geoprocessing in cloud computing platforms – a comparative analysis. International Journal of Digital Earth. 6(4): 404-425.

[50]   Di, L., Yue, P., Ramapriyan, H.K., and King, R., 2013. Introduction to the special issue on geoscience data provenance. IEEE Transactions on Geoscience and Remote Sensing, 51(11), 5062-5064.

[51]   Di, L., Yue, P., Ramapriyan, H.K., and King, R., 2013. Geoscience data provenance: an overview. IEEE Transactions on Geoscience and Remote Sensing, 51(11), 5065-5072.

[52]   Zhai, X., Zhu, X., Lu, X., Yuan, J., Li, M., and Yue, P., 2012. Metadata harvesting and registration in a geospatial sensor web registry. Transactions in GIS. 16(6): 763-780.

[53]   Yue, P., Di, L., Han, W., Zhao, P., Yang, W., and He, L., 2012. Service-oriented approach for geospatial feature discovery. Earth Science Informatics, 5(3-4): 153-165.

[54]   Sun, Z., Yue, P., Lu, X., Zhai, X., and Hu, L., 2012. A task ontology driven approach for live geoprocessing in a service oriented environment. Transactions in GIS. 16(6): 867-884.

[55]   Zhao, P., Foerster, T., and Yue, P., 2012. The geoprocessing web. Computers & Geosciences, 47 (10): 3-12.

[56]   Yue, P., Gong, J., Di, L., and He, L., 2012. Automatic geospatial metadata generation for Earth science virtual data products, GeoInformatica, 16(1): 1-29.

[57]   Sun, Z., Yue, P., and Di, L., 2012. GeoPWTManager: a task-oriented web geoprocessing system. Computers & Geosciences, 47 (10): 34-45.

[58]   Yue, P., Wei, Y., Di, L., He, L., Gong, J., and Zhang, L., 2011. Sharing geospatial provenance in a service-oriented environment, Computers, Environment and Urban Systems, 35 (4): 333-343.

[59]   Yue, P., Gong, J., Di, L., He, L., and Wei, Y., 2011. Integrating semantic web technologies and geospatial catalog services for geospatial information discovery and processing in cyberinfrastructure. GeoInformatica, 15 (2): 273-303.

[60]   Yue, P., Gong, J., Di, L., Yuan, J., Sun, L., Sun, Z., and Wang, Q., 2010. GeoPW: Laying Blocks for the Geospatial Processing Web. Transactions in GIS, 14 (6): 755–772.

[61]   Yue, P., Gong, J., Xiang, L. and Chen, J., 2010. Analysis-enhanced virtual globe for digital Earth. Science China Technological Sciences, 53 (s1): 61-67.

[62]   Yue, P., Gong, J., and Di, L., 2010. Augmenting geospatial data provenance through metadata tracking in geospatial service chaining. Computers & Geosciences, 36 (3): 270-281.

[63]   Boulos, M.N.K., Warren, J., Gong, J., and Yue, P., 2010. Web GIS in practice VIII: HTML5 and the canvas element for interactive online mapping. International Journal of Health Geographics, 2010, 9:14-26.

[64]   Gong, J., Xiang, L., Chen, J., and Yue, P., 2010. Multi-source geospatial information integration and sharing in virtual globes. Science China Technological Sciences, 53 (s1): 1-6.

[65]   Yue, P., Di, L., Yang, W., Yu, G., Zhao, P. and Gong, J., 2009. Semantic web services - based process planning for earth science applications. International Journal of Geographical Information Science, 23 (9): 1139-1163.

[66]   Zhao, P., Di, L., Yu, G., Yue, P., Wei., Y., and Yang, W., 2009. Semantic web-based geospatial knowledge transformation. Computers & Geosciences, 35 (4): 798-808.

[67]   Yue, P., Di, L., Yang, W., Yu, G. and Zhao, P., 2007. Semantics-based automatic composition of geospatial Web services chains. Computers & Geosciences, 33 (5): 649-665.

[68]   乐鹏. 高性能地理计算[M]. 科学出版社，2021.

[69]   乐鹏. 网络地理信息系统和服务[M]. 武汉大学出版社，2011.

[70]   乐鹏, 刘瑞祥, 上官博屹, 曹志鹏, 刘帅旗, 徐翰文. 地理人工智能样本：模型、质量与服务[J]. 武汉大学学报(信息科学版), 2023, 48(10): 1616-1631.

[71]   高凡, 乐鹏, 姜良存, 曹志鹏, 梁哲恒, 上官博屹, 胡磊, 赵帅锋. GeoCube: 面向大规模分析的多源对地观测时空立方体[J]. 遥感学报，2022, 26(6): 1051-1066.

[72]   曹志鹏, 姜良存, 黄秋钧, 乐鹏, 上官博屹, 罗啊玲, 梁哲恒. 利用破坏重建算法进行物流车辆动态调度[J]. 武汉大学学报•信息科学版. 2021, 46(5): 755-765

[73]   李皓, 乐鹏, 姜良存, 张明达，梁哲恒. 矢量地理信息溯源记录组织验证的区块链技术[J]. 测绘学报，2021，50(6)：823-832.

[74]   田晨睿,姜良存,张明达,上官博屹,乐鹏. 结合多层感知器与KM算法的POI匹配方法[J].地理与地理信息科学，2020，36(02):16-21.

[75]   上官博屹, 刘威, 乐鹏, 王密，江昊，颜哲人. 一种面向空间信息网络的星地协同计算迁移方法[J]. 武汉大学学报·信息科学版, 2019, 44(3): 459-466.

[76]   乐鹏, 吴昭炎, 上官博屹. 基于Spark的分布式空间数据存储结构设计与实现[J]. 武汉大学学报•信息科学版, 2018,43(12): 2295-2302 .

[77]   张明达, 乐鹏, 高凡. 组件与服务耦合的地学模型集成方法与实现[J]. 武汉大学学报·信息科学版, 2018, 43(7): 1106-1112.

[78]   上官博屹, 乐鹏. 流计算与传感网集成的城市内涝模型实时感知研究[J]. 地理信息世界, 2018, 25 (03):9-14.

[79]   卜晓倩, 乐鹏, 张明达, 汪林楠. 分布式多源处理集成的地学工作流脚本方法[J]，测绘科学，2016, 41(10)：159-164.

[80]   胡磊, 乐鹏, 龚健雅，张锡宁. 异步地理信息网络处理服务方法研究[J]，武汉大学学报 •信息科学版，2016, 41(5)：679-685.

学生培养：

[1]     国际化培养 (参见Link...)

[2]     实验室常年接收本科生、研究生、博士后，联系邮箱：pyue_whu@163.com

学生毕业去向：

[1]     高校任教：美国乔治梅森大学、武汉大学、武汉理工大学、湖北大学、西北大学、南京信息工程大学…

[2]     事业单位：国家基础地理信息中心、天津测绘院、铁四院、中南电力设计院、武汉市不动产登记中心…

[3]     企业：华为、百度、腾讯、顺丰、中国银联、招商银行、天津航空、北京洛斯达、国电南瑞…

实验室网站：http://geos.whu.edu.cn