  Unreliable predictions can occur when using artificial intelligence (AI)
systems with negative consequences for downstream applications, particularly
when employed for decision-making. Conformal prediction provides a
model-agnostic framework for uncertainty quantification that can be applied to
any dataset, irrespective of its distribution, post hoc. In contrast to other
pixel-level uncertainty quantification methods, conformal prediction operates
without 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 increased
need to report uncertainty alongside point predictions, we bring attention to
the promise of conformal prediction within the domain of Earth Observation (EO)
applications. To accomplish this, we assess the current state of uncertainty
quantification in the EO domain and found that only 20% of the reviewed Google
Earth Engine (GEE) datasets incorporated a degree of uncertainty information,
with unreliable methods prevalent. Next, we introduce modules that seamlessly
integrate into existing GEE predictive modelling workflows and demonstrate the
application 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, we
discuss the opportunities arising from the use of conformal prediction in EO.
We anticipate that the increased availability of easy-to-use implementations of
conformal predictors, such as those provided here, will drive wider adoption of
rigorous uncertainty quantification in EO, thereby enhancing the reliability of
uses such as operational monitoring and decision making.
