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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Bibliografische Detailangaben

Hauptverfasser:	Geethen Singh, Glenn Moncrieff, Zander Venter, Kerry Cawse-Nicholson, Jasper Slingsby, Tamara B. Robinson
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	Nature Portfolio 2024-07-01
Schriftenreihe:	Scientific Reports
Schlagworte:	Satellite Remote sensing Machine learning Conformal prediction Uncertainty quantification
Online-Zugang:	https://doi.org/10.1038/s41598-024-65954-w
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