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Unreliable predictions can occur when using artificial intelligence (AI)systems with negative consequences for downstream applications, particularlywhen employed for decision-making. Conformal prediction provides amodel-agnostic framework for uncertainty quantification that can be applied toany dataset, irrespective of its distribution, post hoc. In contrast to otherpixel-level uncertainty quantification methods, conformal prediction operateswithout requiring access to the underlying model and training dataset,concurrently offering statistically valid and informative prediction regions,all while maintaining computational efficiency. In response to the increasedneed to report uncertainty alongside point predictions, we bring attention tothe promise of conformal prediction within the domain of Earth Observation (EO)applications. To accomplish this, we assess the current state of uncertaintyquantification in the EO domain and found that only 20% of the reviewed GoogleEarth Engine (GEE) datasets incorporated a degree of uncertainty information,with unreliable methods prevalent. Next, we introduce modules that seamlesslyintegrate into existing GEE predictive modelling workflows and demonstrate theapplication of these tools for datasets spanning local to global scales,including the Dynamic World and Global Ecosystem Dynamics Investigation (GEDI)datasets. These case studies encompass regression and classification tasks,featuring both traditional and deep learning-based workflows. Subsequently, wediscuss the opportunities arising from the use of conformal prediction in EO.We anticipate that the increased availability of easy-to-use implementations ofconformal predictors, such as those provided here, will drive wider adoption ofrigorous uncertainty quantification in EO, thereby enhancing the reliability ofuses such as operational monitoring and decision making.

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