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 need to report uncertainty, we bring attention to the promise of conformal prediction within the domain of EO. Unlike previous uncertainty quantification methods, conformal prediction offers statistically valid prediction regions while concurrently supporting any machine learning model and data distribution. To support the need for conformal prediction, we reviewed EO datasets and found that only 22.5% of the datasets incorporated a degree of uncertainty information, with unreliable methods prevalent. Curr

SUBMITTER: Singh G 

PROVIDER: S-EPMC11246475 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature