Summary of Uncertainty Quantification For Probabilistic Machine Learning in Earth Observation Using Conformal Prediction, by Geethen Singh et al.
Uncertainty quantification for probabilistic machine learning in earth observation using conformal prediction
by Geethen Singh, Glenn Moncrieff, Zander Venter, Kerry Cawse-Nicholson, Jasper Slingsby, Tamara B Robinson
First submitted to arxiv on: 12 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Conformal prediction provides a model-agnostic framework for uncertainty quantification that can be applied to any dataset, post hoc. This approach operates without requiring access to the underlying model and training dataset, offering statistically valid and informative prediction regions while maintaining computational efficiency. The method is particularly useful in Earth Observation (EO) applications where unreliable predictions can have negative consequences. To assess the current state of uncertainty quantification in EO, we reviewed Google Earth Engine (GEE) datasets and found that only 20% incorporated a degree of uncertainty information. We introduce modules that seamlessly integrate into existing GEE predictive modeling workflows and demonstrate the application of these tools for various tasks and datasets, including regression and classification. The use of conformal prediction in EO has opportunities to drive wider adoption of rigorous uncertainty quantification, enhancing the reliability of operational monitoring and decision-making. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a world where AI makes decisions, predictions can be unreliable. This can have big consequences! A new way to measure how sure we are about our predictions is called conformal prediction. It’s special because it doesn’t need to know what’s inside the AI model or how it was trained. This helps us make more accurate predictions in things like Earth Observation (EO), where mistakes can be costly. Right now, most EO datasets don’t include uncertainty information, which makes decisions harder. To fix this, we created tools that fit into existing workflows and showed how to use them for different tasks and data sets. By using conformal prediction, we can make more reliable decisions in things like monitoring the environment. |
Keywords
* Artificial intelligence * Classification * Regression
