Approximately half of the global population does not have access to the internet, even though digital connectivity can reduce poverty by revolutionizing economic development opportunities. Due to a lack of data, Mobile Network Operators and governments struggle to effectively determine if infrastructure investments are viable, especially in greenfield areas where demand is unknown. This leads to a lack of investment in network infrastructure, resulting in a phenomenon commonly referred to as the ‘digital divide’. In this paper we present a machine learning method that uses publicly available satellite imagery to predict telecoms demand metrics, including cell phone adoption and spending on mobile services, and apply the method to Malawi and Ethiopia. Our predictive machine learning approach consistently outperforms baseline models which use population density or nightlight luminosity, with an improvement in data variance prediction of at least 40%. The method is a starting point for developing more sophisticated predictive models of infrastructure demand using machine learning and publicly available satellite imagery. The evidence produced can help to better inform infrastructure investment and policy decisions.

尽管数字连接可以通过革新经济发展机会来减少贫困，但大约有一半的全球人口无法使用互联网。由于缺乏数据，移动网络运营商和政府难以有效地确定基础设施投资是否可行，尤其是在需求未知的未开发地区。这导致缺乏对网络基础设施的投资，从而导致通常被称为“数字鸿沟”的现象。在本文中，我们提出了一种机器学习方法，该方法使用可公开获得的卫星图像来预测电信需求量度，包括手机采用率和移动服务支出，并将该方法应用于马拉维和埃塞俄比亚。我们的预测性机器学习方法始终优于使用人口密度或夜光亮度的基线模型，数据方差预测至少提高了40％。该方法是使用机器学习和可公开获得的卫星图像开发基础设施需求的更复杂的预测模型的起点。产生的证据可以帮助更好地为基础设施投资和政策决策提供依据。