14 citations as recorded by crossref.

1. CIDM: A comprehensive inpainting diffusion model for missing weather radar data with knowledge guidance W. Zhang et al. 10.1016/j.isprsjprs.2025.02.001
2. Higher-resolution data improves deep learning-based precipitation nowcasting A. Gong et al. 10.1016/j.atmosres.2025.108010
3. Why we need to focus on developing ethical, responsible, and trustworthy artificial intelligence approaches for environmental science A. McGovern et al. 10.1017/eds.2022.5
4. Multi-resolution reconstruction of longitudinal streambed footprints using embedded sparse convolutional autoencoders Y. Yang et al. 10.1016/j.jhydrol.2025.132852
5. Artificial intelligence reveals past climate extremes by reconstructing historical records É. Plésiat et al. 10.1038/s41467-024-53464-2
6. Faster chemical mapping assisted by computer vision: insights from glass and ice core samples P. Larkman et al. 10.1039/D5AN00325C
7. Mass Estimation and Protection of Network Security Using Inception‐LSTM and Bayesian Attack Behavior Correlation Y. Lei 10.1002/spy2.70059
8. High-Resolution Precipitation Modeling in Complex Terrains Using Hybrid Interpolation Techniques: Incorporating Physiographic and MODIS Cloud Cover Influences K. Alsafadi et al. 10.3390/rs15092435
9. Inpainting Radar Missing Data via Denoising Iterative Restoration W. Zhang et al. 10.1109/JSTARS.2024.3400407
10. Infilling of missing rainfall radar data with a memory-assisted deep learning approach J. Meuer et al. 10.5194/hess-29-3687-2025
11. Improving the Completion of Weather Radar Missing Data with Deep Learning A. Gong et al. 10.3390/rs15184568
12. Super-resolution of three-dimensional temperature and velocity for building-resolving urban micrometeorology using physics-guided convolutional neural networks with image inpainting techniques Y. Yasuda et al. 10.1016/j.buildenv.2023.110613
13. A Radar Data-Driven AI Approach for Rainfall Nowcasting: Towards Flood Preparedness in Urban Regions S. Dandekar et al. 10.1007/s12524-025-02143-w
14. Restoration of Automotive Radar Signals Under Mutual Interference by Digital In-Painting H. Lee et al. 10.1109/TIM.2024.3427748