paper
2025
Extremes & hazards
Observations & remote sensing New model or method
Transformer / ViTCNN / U-NetColorado State University (incl. CIRA) Journal
2025-01-01
022
Jan 2025
Jinkai Tan, Sheng Chen, Haonan Chen
IEEE Transactions on Geoscience and Remote Sensing
Colorado State University (incl. CIRA)
Swin-Unet with storm-morphology loss terms estimates western North Pacific tropical cyclone intensity from Fengyun imagery, MAE 3.71 m/s.
Estimating Tropical Cyclone Intensity via Deep Learning and Storm Morphology Recognition From Satellite Imagery
In one sentence. Swin-Unet with storm-morphology loss terms estimates western North Pacific tropical cyclone intensity from Fengyun imagery, MAE 3.71 m/s.
Labels
- Topic
- Extremes & hazards, Observations & remote sensing
- Type
- New model or method
- Method
- Transformer / ViT, CNN / U-Net
- Domain
- Regional
- Data
- Satellite
- Status
- Research
- Identifiers
- DOI:10.1109/tgrs.2025.3625624 OpenAlex:W4415594361
- Added
- 2026-10-22
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