Leveraging Deep Spatiotemporal Sequence Prediction Network with Self-Attention for Ground-Based Cloud Dynamics Forecasting
Ground-based cloud image features high-spatiotemporal resolution, presenting detailed local cloud structures and valuable weather information, which are crucial for meteorological forecasting. However, the inherent fuzziness and dynamism of ground-based clouds have hindered the development of effect...
Saved in:
Main Authors: | Sheng Li, Min Wang, Minghang Shi, Jiafeng Wang, Ran Cao |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2024-12-01
|
Series: | Remote Sensing |
Subjects: | |
Online Access: | https://www.mdpi.com/2072-4292/17/1/18 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
sAMDGCN: sLSTM-Attention-Based Multi-Head Dynamic Graph Convolutional Network for Traffic Flow Forecasting
by: Shiyuan Zhang, et al.
Published: (2025-01-01) -
Spatiotemporal Correlation Representation based Precise Resource Management in Space-Air-Ground Integrated Network
by: Tianle MAI, et al.
Published: (2024-06-01) -
Unifying spatiotemporal and frequential attention for traffic prediction
by: Qi Guo, et al.
Published: (2025-01-01) -
Spatiotemporal data modeling and prediction algorithms in intelligent management systems
by: Xin Cao, et al.
Published: (2025-02-01) -
Multi-stage refinement network for point cloud completion based on geodesic attention
by: Yuchen Chang, et al.
Published: (2025-01-01)