Puffin：基于粗粒度观测的空间细节概率学习
Puffin: Probabilistic Learning of Spatial Detail From Coarse Observations
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中文总结 AI 辅助
本文提出概率框架Puffin，利用高分辨率卫星嵌入将粗粒度社会经济变量分解为细尺度估计，在德美多类数据上验证其有效性，为统计分解提供了新方法。
中文摘要 AI 辅助
高分辨率社会经济变量对城市规划、公共卫生、灾害响应和资源分配等应用至关重要，但实际中这些变量往往仅以粗空间分辨率观测得到。本文提出Puffin，一种用于统计分解的概率框架，它以高分辨率卫星嵌入为协变量提升粗粒度总量的分辨率。Puffin并非为每个细粒度子区域预测单一值，而是学习概率分布，并通过聚合感知似然进行训练；推理时，Puffin将预测结果以观测到的区域总量为条件，分配到各子区域，得到的细尺度估计与观测聚合值一致，且带有校准后的不确定性，无需细粒度标签用于训练。我们在德国和美国的人口普查、就业、选举数据上，针对人口、就业及其他计数变量评估Puffin，研究统计分解在不同区域、国家和目标下成功或失败的情况。
英文摘要
High-resolution socioeconomic variables are important for applications such as urban planning, public health, disaster response, and resource allocation. In practice, however, these variables are often observed only at a coarse spatial resolution. We introduce Puffin, a probabilistic framework for statistical disaggregation that raises the resolution of coarse totals using high-resolution satellite embeddings as covariates. Instead of predicting a single value for each fine-resolution subregion, Puffin learns a probability distribution and is trained through an aggregation-aware likelihood. At inference, Puffin conditions these predictions on the observed regional total and splits it among the subregions. The resulting fine-scale estimates are consistent with the observed aggregate and come with calibrated uncertainty, without requiring fine-resolution labels for training. We evaluate Puffin on German and US census, employment, and election data across population, jobs, and other count variables, and study when statistical disaggregation succeeds or fails across regions, countries, and targets.
发表机构
- DFKI（德国人工智能研究中心）
机构由 AI 辅助整理，请以论文原文为准。
