阎继宁，男，博士，副教授，硕士生导师，“地大学者”青年优秀人才，国际数字地球学会空间地球大数据专委会秘书长，国际对地观测卫星委员会(CEOS)信息系统与服务工作组(WGISS)成员。主要研究方向包括大规模时序遥感数据管理及智能分析、遥感云计算。近年来主持国家重点研发计划子课题、国家自然科学基金、CCF-蚂蚁科研基金等项目十余项。在ISPRS P&RS、TGRS等期刊发表SCI检索论文40余篇（一作/通讯20篇），ESI高被引论文2篇，出版中英文专著3部，第一作者主编教材1部，获得省部级奖项3项，IEEE JSTARS 最佳论文奖1项。此外，还担任SCI期刊Remote Sensing客座编辑及Reviewer Board Member，ESCI/EI期刊Big Earth Data、Journal of Autonomous Intelligence客座编辑，Resources, Environment and Sustainability (RES)青年编委，IEEE TGRS、TGSI、TBED、IJRS、遥感学报等国内外30余种期刊审稿人。

代表性论文：

[1] Jining Yan, Lizhe Wang, et al. "A time-series classification approach based on change detection for rapid land cover mapping." ISPRS Journal of Photogrammetry and Remote Sensing 158 (2019): 249-262.

[2] Jining Yan, Lizhe Wang, Haixu He, Dong Liang, Weijing Song, Wei Han. Large area land cover changes monitoring with time-series remote sensing images using transferable deep models. IEEE Transactions on Geoscience and Remote Sensing, 2022. DOI: 10.1109/TGRS.2022.3160617.

[3] Jining Yan, Lizhe Wang, Kim-Kwang Raymond Choo and Wei Jie. A cloud-based remote sensing data production system. Future Generation Computer Systems. 2017. http://dx.doi.org/10.1016/j.future.2017.02.044.

[4] Jining Yan, Xiaodao Chen, Yunliang Chen, Dong Liang. Multi-step prediction of land cover from dense time series remote sensing images with temporal convolutional networks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2020). 10.1109/JSTARS.2020.3020839.

[5] Jining Yan, Lizhe Wang. Suitability Evaluation for Products Generation from Multisource Remote Sensing Data. Remote Sensing. 2016, 8(12), 995.

[6] Jining Yan, Lizhe Wang, Lajiao Chen, Lingjun Zhao, and Bomin Huang. A Dynamic Remote Sensing Data Driven Approach for Oil Spill Simulation in the Sea, Remote Sensing. 2015, 7, 7105-7125.

[7] Jining Yan, Kefa Zhou, Dingsheng Liu，Jinlin Wang, Lizhe Wang, Hui Liu. Alteration information extraction using improved relative absorption band-depth images, from HJ_1A HSI data: a case study in Xinjiang Hatu gold ore district，International Journal of Remote Sensing, 2014, 35(18): 6728-6741.

[8] Jining Yan, Lin Mu, Lizhe Wang, et. al. Temporal Convolutional Networks for the Advance Prediction of ENSO. Scientific Reports (2020) 10:8055. https://doi.org/10.1038/s41598-020-65070-5.

[9] J. Yan, Y. Liu, L. Wang, Z. Wang, X. Huang and H. Liu, "An efficient organization method for large-scale and long time-series remote sensing data in a cloud computing environment," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, doi: 10.1109/JSTARS.2021.3110900.

[10] Lizhe Wang, Jining Yan*, Lin Mu, Liang Huang. Knowledge discovery from remote sensing images: A review. WIREs Data Mining Knowl Discov. 2020;e1371. https://doi.org/10.1002/widm.1371.

[11] Chen chen, Jining Yan*, Lizhe Wang, et.al. Classification of urban functional areas from remote sensing images and time-series user behaviour data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021, Vol 14, 1207-1221. 10.1109/JSTARS.2020.3044250.

[12] Lizhe Wang, Yan Ma, Jining Yan, Victor Chang and Albert Y. Zomaya. pipsCloud: High Performance Cloud Computing for Remote Sensing Big Data Management and Processing. Future Generation Computer Systems.http://dx.doi.org/10.1016/j.future.2016.06.009.

[13] J. Yan, J. Liu, L. Wang, D. Liang, Q. Cao, W. Zhang and J. Peng, "Land-cover classification with time-series remote sensing images by complete extraction of multi-scale timing dependency," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, doi: 10.1109/JSTARS.2022.3150430.

[14] H. He, J. Yan*, L. Wang, D. Liang, J. Peng and C. Li, "Bayesian Temporal Tensor Factorization-Based Interpolation for Time Series Remote Sensing Data with Large-Area Missing Observations," in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2022.3140436.

基准数据集：

[1] 土地覆盖时序变化遥感监测基准数据集CUG-FFireMCD1，可以为遥感时间序列变化检测的模型优化和精度验证提供基准。访问链接https://github.com/CUG-BEODL/CUG-FFireMCD

代表性工程研发成果：

遥感云，基于课题组多年的研究积累及863课题项目经验，开发的基于云计算环境的遥感大数据一体化高性能服务平台，包含多数据中心框架下的自动数据集成、高性能组织与存储、快速检索与访问，高性能数据处理，分布式计算框架下的智能分析与深度挖掘、大区域尺度遥感制图等功能。

数字地球实验室

中国地质大学（武汉）数字地球实验室，主要致力于城市遥感、海岛礁遥感、无人机遥感、复杂地质环境遥感，以及遥感图像智能处理等方面的研究。