A Deep Learning Framework for the Detection of Tropical Cyclones From Satellite Images
In one sentence. Mask R-CNN detector, wind speed filter and CNN classifier detect tropical cyclones in satellite images with 97.10% precision on test images.
Abstract
Tropical cyclones (TCs) are the most destructive weather systems that form over the tropical oceans, with 90 storms forming globally every year. The timely detection and tracking of TCs are important for advanced warning to the affected regions. As these storms form over the open oceans far from the continents, remote sensing plays a crucial role in detecting them. Here we present an automated TC detection from satellite images based on a novel deep learning technique. In this study, we propose a multi-staged deep learning framework for the detection of TCs, including, (i) a detector - Mask Region-Convolutional Neural Network (R-CNN), (ii) a wind speed filter, and (iii) a classifier - CNN. The hyperparameters of the entire pipeline is optimized to showcase the best performance using Bayesian optimization. Results indicate that the proposed approach yields high precision (97.10%), specificity (97.59%), and accuracy (86.55%) for test images.
Labels
- Topic
- Extremes & hazards, Observations & remote sensing
- Type
- New model or method
- Method
- CNN / U-Net
- Domain
- Regional
- Data
- Satellite
- Status
- Research
- Identifiers
- DOI:10.1109/lgrs.2021.3131638 OpenAlex:W4246227656
- Added
- 2026-10-22
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