基于合同的地球观测训练：观察服务协议图和覆盖-准确率权衡
Contract-Governed Training for Earth Observation: Observed Service Agreement Graphs and Coverage-Accuracy Trade-offs
AI总结：
本文提出基于合同的地球观测训练方法，通过观察服务协议图（OSAG）优化覆盖与准确率的权衡，减少优先覆盖误差并提升高优先级准确性。
摘要：
地球观测（EO）模型通常在隐含采样策略下进行训练，该策略优化全局准确性但没有明确保证在训练过程中服务的区域、类别或关键任务层。本文介绍了一种针对EO的合同治理训练范式，其中训练样本被分为服务合同——如（数据集、区域、稀有作物指示器）等语义有意义的单元，并为每个合同分配目标服务份额。我们将其实例化为观察服务协议图（OSAG），这是一种轻量级的治理层，它（i）在优化过程中监控合同层面的暴露（覆盖）；（ii）通过合同标准化采样权重驱动经验覆盖向目标份额靠拢；（iii）通过两个调节器暴露明确的准确率-治理权衡：采样混合系数alpha和合同正则化权重lambda_C。我们提供了一个玩具场景下的紧凑理论：OSAG采样将经验覆盖集中到目标；覆盖偏差上界限定服务风险偏差；合同设计（粗略 vs. 精细）调节治理成本。在AVIRIS超光谱场景（印度pine加salinas）和多光谱Sentinel-2 EuroSAT上的实验表明，OSAG可以显著减少优先覆盖误差，同时保持全局准确性并提高高优先级准确性。一个EuroSAT粗略-精细合同消融进一步证明了如何通过语义精细化合同减少每单位治理改进的准确性成本。
英文摘要：
Earth observation (EO) models are frequently trained under implicit sampling policies that optimize global accuracy but provide no explicit guarantees on who (which regions, classes, or mission-critical strata) is being served throughout training. This paper introduces a contract-governed training paradigm for EO in which training samples are grouped into service contracts -- semantically meaningful units such as (dataset, region, rare-crop indicator) -- and each contract is assigned a target service share. We instantiate this paradigm as an Observed Service Agreement Graph (OSAG), a lightweight governance layer that (i) monitors contract-level exposure (coverage) during optimization, (ii) drives empirical coverage toward target shares via contract-normalized sampling weights, and (iii) exposes explicit accuracy-governance trade-offs through two knobs: a sampling mixture coefficient alpha and a contract-regularization weight lambda_C. We provide a compact theory in a toy setting: OSAG sampling concentrates empirical coverage to targets; coverage deviations upper-bound service-risk deviations; and contract design (coarse vs. fine) modulates governance cost. Experiments on AVIRIS hyperspectral scenes (Indian Pines plus Salinas) and multispectral Sentinel-2 EuroSAT demonstrate that OSAG can substantially reduce priority coverage error while maintaining global accuracy and improving high-priority accuracy. A EuroSAT coarse-vs-fine contract ablation further evidences how semantically refined contracts can reduce the accuracy cost per unit of governance improvement.
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