Deep Learning Based Cyclone Intensity Estimation
In one sentence. A capsule neural network estimates tropical cyclone intensity from INSAT-3D satellite images; the abstract reports no quantitative accuracy.
Abstract
Abstract Tropical cyclones are among the most expensive natural disasters on the planet due to the numerous related risks. The first step in tracking a crisis is determining its intensity. A precise diagnostic model for tropical cyclone intensity can therefore save loss of life and property. The primary goal of this study is to evaluate cyclone intensity and identify how harmful it can be, thereby aiding in the prevention of cyclone-related damage.[25] Given that all cyclones form over the ocean, one cannot determine the how changes in the atmosphere of the ocean affects the formation and growth of a cyclone by ground-based measurement instruments alone. Thus, satellite imagery would prove to be beneficial data to determine the intensity. This paper proposes a Capsule Neural Network model to determine the intensity of the cyclone by using the satellite images taken from the INSAT 3D satellite.
Labels
- Topic
- Observations & remote sensing, Extremes & hazards
- Type
- New model or method
- Method
- Other
- Domain
- Regional
- Data
- Satellite
- Status
- Research
- Identifiers
- DOI:10.21203/rs.3.rs-3397684/v1 OpenAlex:W4387410558
- Added
- 2026-10-22
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