Nighttime lights (night-lights) data are useful in predicting gross domestic product (GDP), a key economic indicator used by policymakers and economists. A persistent problem in such prediction is that night-lights under-represent economic activity in rural areas. Attempting to disaggregate night-lights using urban and rural regions is problematic, as the urban–rural dichotomy is increasingly tenuous due to changing economic structures. In response, this article presents a regionalization approach, which is data-driven. Utilizing transfer learning, we trained a model that takes fine spatial resolution daytime satellite sensor imagery and learns an optimal regionalization to disaggregate visible infrared imaging radiometer suite (VIIRS) night-lights for GDP prediction. To make national scale inference feasible, we formulate a novel Monte Carlo importance sampling scheme, and then performed a single-year cross-sectional study across 178 countries using 178 000 images. This achieved an R2 between predicted and actual log10 GDP of 0.86 on the validation set and 0.89 on the whole study area. To benchmark, we perform a subnational study over 396 U.S. counties using 98 500 images in which our method outperformed comparable methods. Interpreting the regionalization, we found that the utility of the urban–rural dichotomy is not supported by the model and argue that seeing the night-lights of some land types as representative of the overall economy is a better way to understand the model.