Human-in-the-Loop Solution for Scoring Economic Development from Geospatial Data

Abstract

Reliable and timely measurements of economic activities are fundamental for understanding economic development and for delivering humanitarian aid and disaster relief to where needed. However, many developing countries still lack reliable data. This paper introduces a novel approach for measuring economic development from high-resolution satellite images in the absence of ground truth statistics. Our method's novelty is that we break down a computationally hard problem into sub-tasks, which involves a human-in-the-loop solution. With the combination of unsupervised learning and the partial orders of dozens of urban versus rural clusters, our method can estimate the economic development scores of over 10,000 satellite grids with less human labor than other baseline approaches (Spearman correlation of 0.851). We demonstrate how to apply our method to both developed and developing economies.