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,缺乏贫穷数据使关键的人道主义组织无法对大规模危机作出反应。目前,社会经济调查是各组织和研究人员大规模采用的唯一衡量和跟踪贫穷的唯一方法。然而,由于无法高效和廉价地收集调查数据,导致贫穷数据存在巨大的时间差距;这些差距严重限制了组织实体从根源上解决贫穷问题的能力。我们提议了一个基于地表温度变化和遥感数据的转移学习模式,以获取有助于预测贫穷率的特征。在贫穷指标数据来源的支持下,机器学习有可能在严格的时间限制内准确估计贫穷率。由于气候变化,较高的气温造成了许多农业障碍、社会经济问题和环境破坏,使发展中国家的家庭陷入贫穷循环。为了找到与温度有关的贫穷模式,对空间贫穷率影响最大,我们使用遥感数据。两步转移模式预测高分辨率卫星图像的温度三角塔,然后提取可用于预测贫穷的图像特征。由此形成的模型在温度预测方面实现了80%的准确性。这一方法利用了丰富的卫星和温度数据模型,以类似的方式测量贫穷状况,并比现有方法进行类似的贫穷预测。</s>