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.

翻译：当使用人工智能（AI）系统时，可能产生不可靠的预测，这会对下游应用（尤其是决策制定）产生负面影响。共形预测提供了一种与模型无关的不确定性量化框架，可事后应用于任何数据集，无论其分布如何。与其他像素级不确定性量化方法相比，共形预测无需访问底层模型和训练数据集，同时提供统计有效且信息丰富的预测区域，并保持计算效率。为满足点预测附随不确定性报告日益增长的需求，我们聚焦共形预测在地球观测（EO）应用领域的前景。为此，我们评估了EO领域不确定性量化的现状，发现仅20%的谷歌地球引擎（GEE）数据集包含一定程度的不确定性信息，且存在不可靠方法普遍使用的问题。我们进一步引入可无缝集成到现有GEE预测建模工作流中的模块，并展示这些工具在涵盖局部到全球尺度数据集（包括Dynamic World和全球生态系统动态调查（GEDI）数据集）中的应用。这些案例研究涵盖回归与分类任务，涉及传统工作流及基于深度学习的工作流。随后，我们探讨了共形预测在EO中的应用机遇。我们预期，易于使用的共形预测器实现（如本文提供的方案）的普及，将推动EO领域对严谨不确定性量化的更广泛采用，从而提升操作监测与决策制定等应用场景的可靠性。