Cui et al. (2026) An Autoencoder-Based Framework for Multimodal Fusion in Forecasting Tropical Cyclone-Induced Sea Surface Height Responses

Identification

• Journal: IEEE Transactions on Geoscience and Remote Sensing
• Year: 2026
• Date: 2026-01-01
• Authors: Hongxing Cui, Xiaowei Gu, Iam Fei Pun, Chao Li, Ibrahim Hoteit
• DOI: 10.1109/tgrs.2026.3653825

Research Groups

This paper proposes an autoencoder-based framework for multimodal data fusion to forecast sea surface height responses induced by tropical cyclones.

Objective

• To develop and evaluate an autoencoder-based framework for multimodal data fusion.
• To improve the forecasting of tropical cyclone-induced sea surface height responses.

Study Configuration

• Spatial Scale: Regions impacted by tropical cyclones.
• Temporal Scale: Forecasting of sea surface height responses during tropical cyclone events.

Methodology and Data

• Models used: Autoencoder-based framework, multimodal fusion techniques.
• Data sources: Multimodal data (specific types not detailed).

Main Results

Contributions

• Introduction of an autoencoder-based framework for forecasting tropical cyclone-induced sea surface height responses.
• Advancement in multimodal data fusion techniques for this specific application.

Funding

Citation

@article{Cui2026AutoencoderBased,
  author = {Cui, Hongxing and Gu, Xiaowei and Pun, Iam Fei and Li, Chao and Hoteit, Ibrahim},
  title = {An Autoencoder-Based Framework for Multimodal Fusion in Forecasting Tropical Cyclone-Induced Sea Surface Height Responses},
  journal = {IEEE Transactions on Geoscience and Remote Sensing},
  year = {2026},
  doi = {10.1109/tgrs.2026.3653825},
  url = {https://doi.org/10.1109/tgrs.2026.3653825}
}