We obtain satellite indicators from a variety of sources, which we categorize into four types, based on their resolution, availability, and accessibility. The publicly available indicators obtainable for the entire country are categorized into, (i) low-resolution indicators, based on imagery resolution ranging from about 0.5km per pixel to about 30m per pixel; and (ii) highresolution imagery whose resolution ranges from about 30m per pixel to 12m per pixel. The proprietary indicators are taken from Engstrom et al. (2017), and are based on very-high resolution imagery acquired from DigitalGlobe covering 55 Divisional Secretariat divisions (one level higher than the village, henceforth referred to as sub-districts). These are divided into two categories: (iii) contextual feature indicators; (iv) object-based indicators quantifying objects such as roofs and cars.

Our results that use Poisson regressions based on variables selected by the least absolute shrinkage and selection operator (LASSO) indicate that satellite indicators accurately predict village-level census population density. Models using publicly available satellite indicators in a full national sample predict density with an out-of-sample _R_<sup>2</sup> of 0.75. Including the very-high resolution indicators and estimating the model in the 55 sub-district sample, the out-of-sample _R_<sup>2</sup> rises to 0.83. Although the public indicators perform well in predicting density in rural as compared to urban areas, the urban-sector prediction accuracy increases substantially (about 0.13 points) when object and contextual features from Engstrom et al. (2017) are added, reflecting in part the heterogeneity in the relationship between population density and builtup area in urban areas.<sup>3</sup> The accuracy of the predictions does not meaningfully change when the size of the training sample is reduced. Results from random forest models produce similar results to the LASSO-based regressions, and corroborate the results across different types of high-resolution resolution-types and sectors.

Finally, we obtain results from a similar Poisson model that uses estimates of population density obtained from the 2012/13 HIES instead of the census. We then compare the out-ofsample predictions from this model with the actual census population densities in villages not covered by the HIES. This yields an out-of-sample _R_<sup>2</sup> of 0.79, Spearman Rank Correlation (SRC) of 0.91, a mean Relative Error (RE) of 37%, and a median RE of 28%. Based on _R_<sup>2</sup> and mean RE, our model is more accurate than the top-down measures: GPW (both years), GHSL, LandScan, and the 2012 WorldPop, and not as accurate as Facebook and WorldPop 2015.<sup>4</sup> The results on these relative accuracies extend to village-level population-count predictions derived by multiplying density predictions by village area. The prediction accuracy remains similar if only indicators that are publicly available are used in the model.

> 3 Rural buildings are mostly residential, but urban buildings tend to contain a heterogeneous mix of commercial and residential buildings. High resolution built-up area measures and object identifiers are better able to capture these heterogeneities in urban areas.

> 4 Using median RE as the benchmark, we report comparable performances to all 2015 estimates, except Facebook and WorldPop 2015.

3