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Machine Learning: Defining Worldwide Cyclone Labels For Training

Abstract

In this paper we present a procedure for labeling both tropical and extratropical cyclones. The procedure is developed based off of a set of strict heuristics for the purpose of creating a worldwide labeled dataset for cyclones. The heuristics are defined from time, pressure, vorticity, and gradient thresholds without an explicit terrain cut-off. The outputted labeled dataset provides a cyclone center and Region of Interest (ROI) for the area of the cyclone at some given time stamp and this is then applied to GOES 15 water vapor imagery for purposes of a training dataset in deep learning network applications.

Article / Publication Data
Active/Online
YES
Available Metadata
Accepted On
July 13, 2018
DOI ↗
Fiscal Year
Published On
September 04, 2018
Publisher Name
IEEE
Submitted On
July 10, 2018
URL ↗
Event

This publication was presented at the following:

Title
2018 - 21st International Conference on Information Fusion (FUSION)
Sponsor
IEEE
Type
Conference
Location
Cambridge - United Kingdom
When
Jul 10, 2018 - Jul 13, 2018

Institutions

Not available

Authors

Authors who have authored or contributed to this publication.