In many developing nations, a lack of poverty data prevents critical humanitarian organizations from responding to large-scale crises. Currently, socioeconomic surveys are the only method implemented on a large scale for organizations and researchers to measure and track poverty. However, the inability to collect survey data efficiently and inexpensively leads to significant temporal gaps in poverty data; these gaps severely limit the ability of organizational entities to address poverty at its root cause. We propose a transfer learning model based on surface temperature change and remote sensing data to extract features useful for predicting poverty rates. Machine learning, supported by data sources of poverty indicators, has the potential to estimate poverty rates accurately and within strict time constraints. Higher temperatures, as a result of climate change, have caused numerous agricultural obstacles, socioeconomic issues, and environmental disruptions, trapping families in developing countries in cycles of poverty. To find patterns of poverty relating to temperature that have the highest influence on spatial poverty rates, we use remote sensing data. The two-step transfer model predicts the temperature delta from high resolution satellite imagery and then extracts image features useful for predicting poverty. The resulting model achieved 80% accuracy on temperature prediction. This method takes advantage of abundant satellite and temperature data to measure poverty in a manner comparable to the existing survey methods and exceeds similar models of poverty prediction.

翻译：在许多发展中国家，贫困数据的缺失阻碍了关键人道主义组织应对大规模危机。目前，社会经济调查是大规模组织和研究者衡量及追踪贫困的唯一实施方法。然而，无法高效低成本地收集调查数据导致贫困数据存在显著的时间断层；这些断层严重限制了组织实体从根源上解决贫困问题的能力。我们提出一种基于地表温度变化和遥感数据的迁移学习模型，以提取有助于预测贫困率的特征。机器学习借助贫困指标数据源的支持，有望在严格的时间限制内准确估算贫困率。气候变化导致的高温引发了众多农业障碍、社会经济问题和环境干扰，使发展中国家家庭陷入贫困循环。为发现对空间贫困率影响最大的温度相关贫困模式，我们使用遥感数据。该两步迁移模型首先从高分辨率卫星图像中预测温度差值，随后提取有助于贫困预测的图像特征。最终模型在温度预测上达到80%的准确率。该方法利用丰富的卫星和温度数据，以与现有调查方法相当的方式测量贫困，并超越了同类贫困预测模型。