This work presents an approach for combining household demographic and living
standards survey questions with features derived from satellite imagery to
predict the poverty rate of a region. Our approach utilizes visual features
obtained from a single-step featurization method applied to freely available
10m/px Sentinel-2 surface reflectance satellite imagery. These visual features
are combined with ten survey questions in a proxy means test (PMT) to estimate
whether a household is below the poverty line. We show that the inclusion of
visual features reduces the mean error in poverty rate estimates from 4.09% to
3.88% over a nationally representative out-of-sample test set. In addition to
including satellite imagery features in proxy means tests, we propose an
approach for selecting a subset of survey questions that are complementary to
the visual features extracted from satellite imagery. Specifically, we design a
survey variable selection approach guided by the full survey and image features
and use the approach to determine the most relevant set of small survey
questions to include in a PMT. We validate the choice of small survey questions
in a downstream task of predicting the poverty rate using the small set of
questions. This approach results in the best performance -- errors in poverty
rate decrease from 4.09% to 3.71%. We show that extracted visual features
encode geographic and urbanization differences between regions.

通过结合家庭人口统计和生活水平调查问题以及从卫星图像中提取的特征，预测一个地区的贫困率。我们的方法利用 10 米每像素的 Sentinel-2 地表反射卫星图像，并使用单步特征化方法获得视觉特征。这些视觉特征与十个调查问题结合在一起，通过代理手段测试（PMT）来估计一个家庭是否处于贫困线以下。我们证明了加入视觉特征可以将贫困率估计的均方误差从 4.09% 降低到 3.88%。除了将卫星图像特征包含在代理手段测试中，我们还提出了一种选择与从卫星图像中提取的视觉特征互补的一小部分调查问题的方法。具体来说，我们设计了一种由整个调查和图像特征引导的调查变量选择方法，并使用该方法确定一小组最相关的调查问题，以纳入代理手段测试中。我们验证了这些小调查问题的选择，在使用这小组问题预测贫困率的下游任务中表现最佳，贫困率的误差从 4.09% 降低到 3.71%。我们还证明了提取的视觉特征编码了地理和城市化之间的差异。