  Building coverage statistics provide crucial insights into the urbanization,
infrastructure, and poverty level of a region, facilitating efforts towards
alleviating poverty, building sustainable cities, and allocating infrastructure
investments and public service provision. Global mapping of buildings has been
made more efficient with the incorporation of deep learning models into the
pipeline. However, these models typically rely on high-resolution satellite
imagery which are expensive to collect and infrequently updated. As a result,
building coverage data are not updated timely especially in developing regions
where the built environment is changing quickly. In this paper, we propose a
method for estimating building coverage using only publicly available
low-resolution satellite imagery that is more frequently updated. We show that
having a multi-node quantile regression layer greatly improves the model's
spatial and temporal generalization. Our model achieves a coefficient of
determination ($R^2$) as high as 0.968 on predicting building coverage in
regions of different levels of development around the world. We demonstrate
that the proposed model accurately predicts the building coverage from raw
input images and generalizes well to unseen countries and continents,
suggesting the possibility of estimating global building coverage using only
low-resolution remote sensing data.
