李盼乐，男，1992年2月，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，主要从事遥感图像解译方向的研究、主讲《深度学习及应用》。邮箱：13137052075@163.com。在高分辨率图像分类、噪声标签鲁棒方法等方面积累了丰富的经验。目前已发表高水平论文11篇，其中以第一作者发表SCI论文五篇，四篇为中科院一区论文。前期参与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. 面向超算的黄河模拟器构建与服务关键技术研究，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）（201400210900），2021年1月-2023年12月,在研、参与
2. 美丽青藏建设气象条件贡献率评价系统研发,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（2019QZKK0106）,2019/11-2024/10,在研、参与
3. 全球综合观测大数据多维多尺度可视化引擎构建,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（2018YFB0505000）、2018.05-2022.04、在研、参与
科研论文成果：
1. 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.
2. 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.
3. 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.
4. 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.
5. 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.
6. 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.
7. 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.
8. 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
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，陈明扬，李盼乐，田智慧，周广胜. 结合DCNN与短距条件随机场的遥感影像道路提取. 武汉大学学报(信息科学版), 1-14, 2023.
