Uncertainty quantification for probabilistic machine learning in earth observation using conformal prediction

Abstract Machine learning is increasingly applied to Earth Observation (EO) data to obtain datasets that contribute towards international accords. However, these datasets contain inherent uncertainty that needs to be quantified reliably to avoid negative consequences. In response to the increased ne...

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Principais autores:	Geethen Singh, Glenn Moncrieff, Zander Venter, Kerry Cawse-Nicholson, Jasper Slingsby, Tamara B. Robinson
Formato:	Artigo
Idioma:	Inglês
Publicado:	Nature Portfolio 2024-07-01
Series:	Scientific Reports
Assuntos:	Satellite Remote sensing Machine learning Conformal prediction Uncertainty quantification
Acceso en liña:	https://doi.org/10.1038/s41598-024-65954-w
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