Deepti: Deep-Learning-Based Tropical Cyclone Intensity Estimation System
| Autor: | Brian Freitag, Manil Maskey, Muthukumaran Ramasubramanian, Rahul Ramachandran, Daniel J. Cecil, Drew Bollinger, Iksha Gurung, Aaron Kaulfus, Jeffrey Miller |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Atmospheric Science
machine learning lifecycle 010504 meteorology & atmospheric sciences Meteorology Mean squared error Computer science Geophysics. Cosmic physics 02 engineering and technology 01 natural sciences Wind speed wind speed estimation research to production 0202 electrical engineering electronic engineering information engineering Range (statistics) Satellite imagery Computers in Earth Sciences Natural disaster TC1501-1800 0105 earth and related environmental sciences QC801-809 Deep learning Visualization Ocean engineering model interpretation Microwave imaging 020201 artificial intelligence & image processing Tropical cyclone |
| Zdroj: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 13, Pp 4271-4281 (2020) |
| ISSN: | 2151-1535 1939-1404 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|