Li et al. (2026) Adapting Video Foundation Models for Spatiotemporal Wildfire Forecasting via Cross-Modal Progressive Fine-Tuning

Identification

• Journal: IEEE Transactions on Geoscience and Remote Sensing
• Year: 2026
• Date: 2026-01-01
• Authors: Wenwen Li, Chia-Yu Hsu, Sizhe Wang
• DOI: 10.1109/tgrs.2026.3652453

Research Groups

Not specified in the provided text.

Short Summary

This paper focuses on adapting video foundation models using a cross-modal progressive fine-tuning approach to enhance spatiotemporal forecasting of wildfires.

Objective

• To develop and evaluate a methodology for adapting video foundation models to improve spatiotemporal wildfire forecasting through cross-modal progressive fine-tuning.

Study Configuration

• Spatial Scale: Not specified in the provided text, but implied to be relevant to geographical regions affected by wildfires.
• Temporal Scale: Not specified in the provided text, but implied to be for future prediction of wildfire events.

Methodology and Data

• Models used: Video Foundation Models, Cross-Modal Progressive Fine-Tuning.
• Data sources: Not specified in the provided text, but likely involves video data, satellite imagery, and potentially other environmental data relevant to wildfire prediction.

Main Results

Not specified in the provided text.

Contributions

Not specified in the provided text.

Funding

Not specified in the provided text.

Citation

@article{Li2026Adapting,
  author = {Li, Wenwen and Hsu, Chia-Yu and Wang, Sizhe},
  title = {Adapting Video Foundation Models for Spatiotemporal Wildfire Forecasting via Cross-Modal Progressive Fine-Tuning},
  journal = {IEEE Transactions on Geoscience and Remote Sensing},
  year = {2026},
  doi = {10.1109/tgrs.2026.3652453},
  url = {https://doi.org/10.1109/tgrs.2026.3652453}
}