ClimateNet: an expert-labeled open dataset and deep learning architecture for enabling high-precision analyses of extreme weather

Identifying, detecting, and localizing extreme weather events is a crucial first step in understanding how they may vary under different climate change scenarios. Pattern recognition tasks such as classification, object detection, and segmentation (i.e., pixel-level classification) have remained cha...

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Veröffentlicht in:	Geoscientific Model Development Jg. 14; H. 1; S. 107 - 124
Hauptverfasser:	Kashinath, Karthik, Mudigonda, Mayur, Kim, Sol, Kapp-Schwoerer, Lukas, Graubner, Andre, Karaismailoglu, Ege, von Kleist, Leo, Kurth, Thorsten, Greiner, Annette, Mahesh, Ankur, Yang, Kevin, Lewis, Colby, Chen, Jiayi, Lou, Andrew, Chandran, Sathyavat, Toms, Ben, Chapman, Will, Dagon, Katherine, Shields, Christine A., O'Brien, Travis, Wehner, Michael, Collins, William
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Katlenburg-Lindau Copernicus GmbH 08.01.2021 Copernicus Publications, EGU Copernicus Publications
Schlagworte:	Algorithms Architecture Atmospheric models Classification Climate change Climate change scenarios Climate models Climate science Climatic data Climatic extremes Computer vision Cyclones Datasets Deep learning El Nino Empirical analysis ENVIRONMENTAL SCIENCES Extreme weather Global temperature changes Heuristic Hurricanes Informatics Labeling Machine learning Machine vision Object recognition open climate campaign Pattern recognition Pixels Problem solving Scientists Sea level Segmentation Simulation Statistical methods Success Training Transfer learning Tropical atmosphere Tropical climate Tropical cyclones Variables Weather Weather patterns
ISSN:	1991-9603, 1991-959X, 1991-962X, 1991-9603, 1991-962X
Online-Zugang:	Volltext
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