发表机构
Chalmers University of Technology; University of Gothenburg; Linköping University; Karlstad University; Massachusetts Institute of Technology（查尔姆斯理工大学; 哥德堡大学; 林雪平大学; 卡尔斯塔德大学; 麻省理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对非洲高分辨率贫困数据不足的问题，提出不确定性感知的EO-ML贫困制图方法，生成统计可靠的预测区间，还开发了风险可控的援助分配程序，为政策制定提供可靠补充。
AI 中文摘要
尽管高分辨率贫困数据对政策制定和研究至关重要，但非洲大部分地区仍缺乏这类数据。结合地球观测（EO）影像的机器学习（ML）近来成为补充此类数据的方式，即对未直接测量的区域进行贫困预测。但要可靠应用，决策者和分析人员需保证不会被预测误差误导。为此，我们开发了一种不确定性感知的EO-ML贫困制图方法，基于分位数回归与新型共形预测。我们使用时空Transformer，对非洲社区级国际财富指数估计值生成预测区间，该区间在统计上保证达到所需覆盖率。我们方法的点预测性能与现有最优水平相当，尽管其R²达0.75，预测区间却比预期更宽。不过，其他相似准确率的模型可能存在相当的不确定性，这指向一个固有局限：即便EO-ML具备极高解释力，也不能直接用于政策制定，比如设计贫困瞄准项目。为应对这一挑战，我们开发了一种程序，利用地面调查数据和模型预测高效分配援助，同时可证明确保排除合格社区的风险低于预设水平。在模拟中，该方法比其他策略为每位合格受援者提供了更多援助，证明EO-ML确实可成为传统数据源的可靠补充——只要方法
英文摘要
Despite their critical importance for policy and research, high-resolution poverty data remain limited across much of Africa. Machine learning (ML) with earth observation (EO) imagery has recently emerged as a way to supplement these data by predicting (i.e., estimating) poverty where it has not been directly measured. Yet to be used reliably, decision-makers and analysts need assurances that they will not be misled by the errors in these predictions. To meet this need, we develop an uncertainty-aware EO-ML method for poverty mapping based on simultaneous quantile regression and a novel form of conformal prediction. Using a spatiotemporal transformer trained on sequences of Landsat and nighttime-light images, we produce prediction intervals for neighborhood-level International Wealth Index estimates across Africa which are statistically guaranteed to achieve their desired coverage rates. While our method's point-prediction performance matches the state of the art, its prediction intervals are wider than might be expected given its high $R^2$ of $0.75$. However, other models of similar accuracy likely suffer from comparable uncertainty, pointing to an inherent limitation: even with its remarkably high explanatory power, EO-ML cannot naively be relied upon for policy-making, such as when designing poverty-targeting programs. To handle this challenge, we develop a procedure to efficiently allocate aid using both ground-truth surveys and model predictions while provably ensuring the risk of excluding eligible neighborhoods remains below a prespecified level. In simulations, this approach delivers substantially more aid per eligible recipient than other strategies, thereby demonstrating that EO-ML can indeed be a reliable supplement to traditional data sources.
CommentsThis manuscript is currently under consideration at the Proceedings of the National Academy of Sciences (PNAS)
