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

• URL: http://arxiv.org/abs/2401.06421v1
• Date: Fri, 12 Jan 2024 07:31:21 GMT
• Title: Uncertainty quantification for probabilistic machine learning in earth observation using conformal prediction
• Authors: Geethen Singh, Glenn Moncrieff, Zander Venter, Kerry Cawse-Nicholson, Jasper Slingsby and Tamara B Robinson
• Abstract summary: Unreliable predictions can occur when using artificial intelligence (AI) systems with negative consequences for downstream applications. Conformal prediction provides a model-agnostic framework for uncertainty quantification that can be applied to any dataset. In response to the increased need to report uncertainty alongside point predictions, we bring attention to the promise of conformal prediction in Earth Observation applications.
• Score: 0.22265536092123003
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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