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在地球观测中使用符合性预测进行概率机器学习的不确定性量化
Geethen Singh1, Glenn Moncrieff2,3, Zander Venter4
1Department of Botany and Zoology, Centre for Invasion Biology, Stellenbosch University, Stellenbosch, South Africa. Geethen.singh@gmail.com.
Scientific reports
|July 13, 2024
概括
合规预测为地球观测 (EO) 数据提供可靠的不确定性量化,解决当前方法的局限性. 新的谷歌地球引擎模块整合了这些工具,提高了EO应用程序的可靠性.
科学领域:
- 地球观测 (EO) 是指对地球进行观测.
- 机器学习 机器学习
- 不确定性定量化 不确定性定量化
背景情况:
- 对EO数据的机器学习对于国际协议至关重要,但受到不可靠的不确定性量化影响.
- 现有的方法往往无法为EO数据集提供统计学上有效的不确定性估计.
- 只有22.5%的EO数据集包含不确定性信息,其中普遍存在不可靠的技术.
研究的目的:
- 引入符合性预测作为一个统计学上合理的方法来量化EO的不确定性.
- 解决对EO数据处理中可靠不确定性估计的需求.
- 促进将不确定性量化整合到现有的EO机器学习工作流程中.
主要方法:
- 开发了谷歌地球引擎的原生模块,用于符合性预测,将计算带到数据中.
- 应用于各种EO应用的合规预测,包括回归和分类任务.
- 审查了现有的EO数据集,以评估当前的不确定性量化实践.
主要成果:
- 合规预测提供了统计学上有效的预测区域,适用于任何机器学习模型和数据分布.
- 开发的谷歌地球引擎模块使得高效的,数据原生不确定性量化.
- 在各种EO应用和尺度中展示了符合性预测的多功能性和可扩展性.
结论:
- 可访问的符合性预测工具将推动更广泛地采用EO的严格不确定性量化.
- 增强的不确定性量化提高了下游EO应用程序的可靠性,包括监控和决策.
- 开发的模块简化了将不确定性量化集成到EO的传统和深度学习模型中.
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