Summary of How Certain Are Uncertainty Estimates? Three Novel Earth Observation Datasets For Benchmarking Uncertainty Quantification in Machine Learning, by Yuanyuan Wang et al.
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning
by Yuanyuan Wang, Qian Song, Dawood Wasif, Muhammad Shahzad, Christoph Koller, Jonathan Bamber, Xiao Xiang Zhu
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV)
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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 A novel approach to uncertainty quantification (UQ) is proposed for Earth observation (EO) machine learning models, which are inherently uncertain. The UQ methods existing for machine learning models have not been thoroughly evaluated on EO datasets. To address this gap, three benchmark datasets are introduced, designed specifically for UQ in EO machine learning models. These datasets cover regression, image segmentation, and scene classification tasks, enabling transparent comparison of different UQ methods. The datasets’ creation and characteristics, including data sources, preprocessing steps, and label generation, are described, with a focus on calculating reference uncertainty. Baseline performance of several machine learning models is showcased on each dataset, highlighting their utility for model development and comparison. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary For Earth observation, scientists need to understand how certain their findings are. Machine learning models help analyze images from space or the ground, but they’re not perfect and can be uncertain too. To fix this, researchers created three special datasets that test different ways to measure uncertainty in machine learning models used for Earth observation. These datasets cover different types of tasks, like predicting numbers or classifying images. This makes it easier to compare how well different methods work. |
Keywords
» Artificial intelligence » Classification » Image segmentation » Machine learning » Regression
