Model-Agnostic Predictive Uncertainty for Earth Observation Applications

Kurzfassung

AI models have become indispensable across scientific disciplines, including Earth Observation (EO), where they play a critical role in applications such as climate monitoring, land cover classification and disaster response. However, despite their predictive capabilities, many of these models often lack explicit measures of predictive uncertainty in their outputs. This absence of rigorous prediction intervals limits model interpretability and trustworthiness, posing challenges for risk management and operational deployment. Conformal Prediction (CP) offers a mathematically robust framework to address these issues by providing distribution-free prediction regions with predefined coverage guarantees. Unlike Bayesian or ensemble-based methods, which often introduce high computational costs or require architectural modifications, CP is model-agnostic and computationally efficient, serving as a low-complexity wrapper. These advantages make CP particularly well-suited for scalable, real-world EO applications. In this work, we apply split conformal prediction and adaptive prediction sets to quantify uncertainty in EO-based predictive models. Our experiments across multiple model architectures demonstrate that these methods provide consistent uncertainty estimates while maintaining provable coverage guarantees. By leveraging CP within the constraints of EO applications, we aim to enhance model reliability and decision support, ensuring trustworthy AI-driven geospatial analysis.