Object Recognition for Economic Development from Daytime Satellite Imagery
This addresses the problem of outdated and incomplete subnational economic data for developing countries, offering a scalable alternative to traditional costly methods.
The paper tackles the scarcity of reliable infrastructure data in developing countries by proposing a novel method to extract infrastructure features from high-resolution satellite imagery, showing strong improvements in predictive accuracy.
Reliable data about the stock of physical capital and infrastructure in developing countries is typically very scarce. This is particular a problem for data at the subnational level where existing data is often outdated, not consistently measured or coverage is incomplete. Traditional data collection methods are time and labor-intensive costly, which often prohibits developing countries from collecting this type of data. This paper proposes a novel method to extract infrastructure features from high-resolution satellite images. We collected high-resolution satellite images for 5 million 1km $\times$ 1km grid cells covering 21 African countries. We contribute to the growing body of literature in this area by training our machine learning algorithm on ground-truth data. We show that our approach strongly improves the predictive accuracy. Our methodology can build the foundation to then predict subnational indicators of economic development for areas where this data is either missing or unreliable.