乔梦佳，女，1996年，助理研究员，硕士生导师，毕业于郑州大学计算机与人工智能学院，主要从事遥感大数据智能挖掘的研究、主讲《现代遥感》。邮箱：qiaomjj@zzu.edu.cn。在遥感数据时空挖掘、农作物智能估产方法等方面积累了丰富的经验。目前已发表高水平论文12篇，其中以第一作者发表SCI二区以上论文3篇。主持河南省中央引导地方科技发展资金项目子课题，参与国家自然科学基金面上项目、面向超算的黄河模拟器构建与服务关键技术研究，美丽青藏建设气象条件贡献率评价系统研发，全球综合观测大数据多维多尺度可视化引擎构建等多项国家级或省部级项目。
主要科研项目情况：
1. 河南省中央引导地方科技发展资金项目子课题，小麦智能化感知与灌溉一体化管理系统示范推广，2023/09-2025/12，主持
2. 国家自然科学基金项目, 地表时空异质性干扰下的非平衡复杂场景冬小麦叶面积指数反演（42371358），2024/01-2027/12，参与
3. 河南省重大科技专项（国家超级计算郑州中心创新生态系统建设科技专项），面向超算的黄河模拟器构建与服务关键技术研究（201400210900），2021/01-2023/12，参与
4. 第二次青藏高原综合科学考察研究“西风-季风协同作用及其环境效应”项目子专题，美丽青藏建设气象条件贡献率评价系统研发（2019QZKK0106）, 2019/11-2024/10，参与
5. 国家重点研发计划“全球对地观测成果管理及共享服务系统关键技术研究”子课题，全球综合观测大数据多维多尺度可视化引擎构建（2018YFB0505000）、2018.05-2022.04、参与
科研论文成果：
1. Qiao M, He X, Cheng X, Li P, et al. KSTAGE: A knowledge-guided spatial-temporal attention graph learning network for crop yield prediction[J]. Information Sciences, 619:19-37, 2023.
2. Qiao M, He X, Cheng X, Li P et al. Crop Yield Prediction from Multi-spectral, Multi-temporal Remotely Sensed Imagery using Recurrent 3D Convolutional Neural Networks[J]. International Journal of Applied Earth Observation and Geoinformation, 2021, 102, 102436.
3. Qiao M, He X, Cheng X, Li P, et al. Exploiting Hierarchical Features for Crop Yield Prediction Based on 3-D Convolutional Neural Networks and Multikernel Gaussian Process[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021,14, 4476-4489
4. Li P, He X, et al. An Improved Categorical Cross Entropy for Remote Sensing Image Classification Based on Noisy Labels[J]. Expert Systems with Applications, 205:117296, 2022.
5. Li P, He X, et al. Exploring Label Probability Sequence to Robustly Learn Deep Convolutional Neural Networks for Road Extraction with Noisy Datasets[J]. IEEE Transactions on Geoscience and Remote Sensing, 60:1-18, 2022.
6. Li P, He X, et al. Robust Deep Neural Networks for Road Extraction From Remote Sensing Images[J]. IEEE Transactions on Geoscience and Remote Sensing, 59:6182-6197, 2021.
7. Li P, He X, et al. Exploring multiple crowdsourced data to learn deep convolutional neural networks for road extraction[J]. International Journal of Applied Earth Observation and Geoinformation, 104:102544, 2021.
8. Li P, Tian Z, et al. LR‐RoadNet: A long‐range context‐aware neural network for road extraction via high‐resolution remote sensing images[J]. IET Image Processing, 15:3239-3253, 2021.
9. Cheng X, He X, Qiao M, Li P, et al. Multi-view Graph Convolutional Network with Spectral Component Decompose for Remote Sensing Images Classification[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2022 (Early Access, https://ieeexplore.ieee.org/abstract/document/9970571).
10. Cheng X., He X, Qiao M, Li P, et al. Enhanced Contextual Representation with Deep Neural Networks for Land Cover Classifification Based on Remote Sensing Images[J]. International Journal of Applied Earth Observation and Geoinformation, 2022, 107, 102706.
11. 赫晓慧,张乐涵,乔梦佳,等.基于PROSAIL混合反演模型的MODIS LAI产品改进及评估[J].生态学报.
12. 赫晓慧,罗浩田,乔梦佳,等.基于CNN-RNN网络的中国冬小麦估产[J].农业工程学报,2021,37(17):124-132.
