  This paper addresses the challenge of obtaining precise demographic
information at a fine-grained spatial level, a necessity for planning localized
public services such as water distribution networks, or understanding local
human impacts on the ecosystem. While population sizes are commonly available
for large administrative areas, such as wards in India, practical applications
often demand knowledge of population density at smaller spatial scales. We
explore the integration of alternative data sources, specifically
satellite-derived products, including land cover, land use, street density,
building heights, vegetation coverage, and drainage density. Using a case study
focused on Bangalore City, India, with a ward-level population dataset for 198
wards and satellite-derived sources covering 786,702 pixels at a resolution of
30mX30m, we propose a semiparametric Bayesian spatial regression model for
obtaining pixel-level population estimates. Given the high dimensionality of
the problem, exact Bayesian inference is deemed impractical; we discuss an
approximate Bayesian inference scheme based on the recently proposed
max-and-smooth approach, a combination of Laplace approximation and Markov
chain Monte Carlo. A simulation study validates the reasonable performance of
our inferential approach. Mapping pixel-level estimates to the ward level
demonstrates the effectiveness of our method in capturing the spatial
distribution of population sizes. While our case study focuses on a demographic
application, the methodology developed here readily applies to count-type
spatial datasets from various scientific disciplines, where high-resolution
alternative data sources are available.
