combines survey and satellite data to generate local estimates of population density, and by extension, population counts. Instead of relying on the census, which becomes outdated over time, the proposed method exploits the updated demographic information in surveyed areas from existing periodic household surveys, and the widespread coverage and granular information offered by satellite imagery. The method predicts population density in non-surveyed areas, and in between-census years by employing updated survey and satellite data. This technique is applied in the context of Sri Lanka using the Household Income and Expenditure Survey (HIES), a nationally-representative household survey. The satellite indicators include those derived from both low- and high-resolution satellite imagery for the entire country, and additionally, object and contextual features derived from very-high resolution imagery for a randomly selected portion of the country. These indicators are used to predict population density at the Gram Niladhari (GN) division, the lowest administrative level in Sri Lanka. The GN division is similar in size to a village in many developing country settings, and we henceforth, for ease of exposition, refer to GN divisions as “villages”.

To motivate this approach, we begin by documenting the inconsistency of existing “topdown” population products at the village level, both with each other and with the census. We then address three questions that shed light on the ability of indicators derived from satellite imagery to predict population density. First, how accurately do satellite data predict census population density at the local level? Second, does including indicators derived from high and very high-resolution satellite imagery substantially improve the predictive power of the model? Third, how does the size of the training data affect prediction accuracy? We then implement the “bottom-up” approach, and finally ask how accurate are out-of-sample predictions that are derived from the HIES and satellite-imagery-based model, compared to the existing “top-down” products?

The “top-down” products considered include, WorldPop for the years 2010 and 2015, the Global Human Settlement Layer (GHSL) for 2014, High Resolution Settlement Layer (HRSL) created by Facebook for 2015, the Center for International Earth Science Information Network’s (CIESIN) Gridded Population of the World (GPW) for 2010 and 2015, and LandScan for 2010. We use simple statistical measures of association to confirm that top-down estimates are poorly correlated with each other and with the census at the village level. WorldPop 2015 and Facebook are the exceptions, because they use high-resolution satellite imagery and are calibrated to the latest census data at geographically fine levels.<sup>2</sup> However, since even the most accurate population products use the census to redistribute population, they may quickly become outdated as the census ages, necessitating “bottom-up” methods to track changes more frequently.

> 2 At Divisional Secretariat level (one level above the village), however, all estimates are highly correlated with each other and with the census, implying that accuracy at the coarser levels is easier to achieve.

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