Deepti: Deep-Learning-Based Tropical Cyclone Intensity Estimation System

Tropical cyclones are one of the costliest natural disasters globally because of the wide range of associated hazards. Thus, an accurate diagnostic model for tropical cyclone intensity can save lives and property. There are a number of existing techniques and approaches that diagnose tropical cyclon...

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Bibliographic Details
Published in:IEEE journal of selected topics in applied earth observations and remote sensing 2020-01, Vol.13, p.4271-4281
Main Authors: Maskey, Manil, Ramachandran, Rahul, Ramasubramanian, Muthukumaran, Gurung, Iksha, Freitag, Brian, Kaulfus, Aaron, Bollinger, Drew, Cecil, Daniel J., Miller, Jeffrey
Format: Article
Language:English
Subjects:
Cyclones
Deep learning
Diagnostic systems
Disasters
Earth Resources And Remote Sensing
End users
Estimation
Hurricanes
Imagery
Infrared imagery
machine learning lifecycle
Microwave imaging
Microwave measurement
Microwave theory and techniques
model interpretation
Natural disasters
research to production
Satellite imagery
Satellites
Spaceborne remote sensing
Tropical climate
Tropical cyclones
Wind speed
wind speed estimation
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Summary:Tropical cyclones are one of the costliest natural disasters globally because of the wide range of associated hazards. Thus, an accurate diagnostic model for tropical cyclone intensity can save lives and property. There are a number of existing techniques and approaches that diagnose tropical cyclone wind speed using satellite data at a given time with varying success. This article presents a deep-learning-based objective, diagnostic estimate of tropical cyclone intensity from infrared satellite imagery with 13.24-kn root mean squared error. In addition, a visualization portal in a production system is presented that displays deep learning output and contextual information for end users, one of the first of its kind.
ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2020.3011907