  High-resolution daytime satellite imagery has become a promising source to
study economic activities. These images display detailed terrain over large
areas and allow zooming into smaller neighborhoods. Existing methods, however,
have utilized images only in a single-level geographical unit. This research
presents a deep learning model to predict economic indicators via aggregating
traits observed from multiple levels of geographical units. The model first
measures hyperlocal economy over small communities via ordinal regression. The
next step extracts district-level features by summarizing interconnection among
hyperlocal economies. In the final step, the model estimates economic
indicators of districts via aggregating the hyperlocal and district
information. Our new multi-level learning model substantially outperforms
strong baselines in predicting key indicators such as population, purchasing
power, and energy consumption. The model is also robust against data shortage;
the trained features from one country can generalize to other countries when
evaluated with data gathered from Malaysia, the Philippines, Thailand, and
Vietnam. We discuss the multi-level model's implications for measuring
inequality, which is the essential first step in policy and social science
research on inequality and poverty.
