Uncertainty quantification for probabilistic machine learning in earth observation using conformal prediction
(2401.06421)Abstract
Unreliable predictions can occur when using 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.
Overview
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AI has advanced geospatial applications in EO, vital for SDGs and conservation, but data uncertainties compromise reliability.
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Traditional UQ methods like model ensembles lack coverage guarantees and are computationally demanding, with EO studies seldom reporting uncertainty.
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Conformal prediction offers a model-agnostic, computationally efficient approach to reliable UQ without requiring in-depth model or data knowledge.
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Case studies demonstrate the method's practicality, and new tools facilitate its integration into EO workflows for better data-informed decisions.
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Conformal prediction enhances trust in AI, ensures model reliability, and is key for the future of EO in achieving environmental objectives.
Introduction
The ability of AI to process and interpret Earth Observation (EO) data has transformed the landscape of geospatial applications. These advancements are critical for achieving Sustainable Development Goals (SDGs) and informing conservation strategies. However, the reliability of AI-based decision-making systems can be compromised by data uncertainties, which, if not correctly accounted for, can lead to suboptimal or even erroneous outcomes. To address this vital issue, researchers have introduced the concept of conformal prediction as a compelling solution for Uncertainty Quantification (UQ) in EO. This paper reviews UQ's current state within the EO domain and demonstrates the use of introduced Earth Engine tools for UQ, discussing the potential of conformal prediction to refine EO applications significantly.
Uncertainty in Earth Observation
When utilizing EO data for environmental monitoring and management, the quality of decisions hinges on the accuracy and uncertainty of the underlying data. Traditional methods of quantifying uncertainty have been predominantly focused on model ensembles and quantile regressions, allowing for the estimation of prediction variance. However, these methods often lack valid coverage guarantees and are computationally intensive. Only a small fraction of studies in EO report uncertainty levels, illustrating a gap between current practices and the ideal scenario where predictions come with quantified degrees of certainty or uncertainty.
Conformal Prediction and Its Advantages
As a model-agnostic framework that requires no retraining, conformal prediction offers a new avenue for reliable UQ. It provides statistically valid prediction regions or sets, applicable to diverse machine learning tasks while ensuring computational efficiency. The method does not necessitate knowledge of the model's internals or the data's distribution, a considerable advantage for a range of applications. For instance, in the classification of land cover or the estimation of canopy height, conformal prediction can communicate quality assessments and guide decision-makers more effectively.
Case Studies and Tools for Implementation
The practical utility of conformal prediction is illustrated through case studies that span various geographic extents and machine learning tasks. These range from global land cover classification, as with the Dynamic World dataset, to the more localized detection of invasive tree species. The paper emphasizes the importance of complementing point predictions with UQ, allowing for a better-informed and trustful utilization of EO data. Additionally, the research introduces user-friendly modules designed to integrate conformal prediction into existing workflows seamlessly. These open-source tools, provided in both Python and JavaScript formats, are set to catalyze the wide adoption of robust UQ in EO.
Conclusion
Conformal prediction stands as an innovative and promising method to facilitate the widespread use of probabilistic machine learning in EO. It bridges the gap between data-driven decision-making and the necessity for comprehensible uncertainty information. By fostering greater trust in AI systems, enhancing model reliability, and providing actionable insights into prediction quality, conformal prediction methods are poised to play a pivotal role in the future of EO and the attainment of global environmental goals.
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