Small-area population estimation is an important task that has received considerable attention from the remote-sensing community in the past four decades. The wealth of related studies reveals that the notion of living space had been considered a key linkage between population and remote-sensing measurements. Unfortunately, a formal definition for this important variable has proved difficult, due, in part, to the relatively coarse spatial resolution of the remote-sensing data used for population estimation. The advent of airborne Light Detection And Ranging (LiDAR) sensors for measuring elevation at fine spatial resolutions has provided new opportunities for considering the three-dimensional nature of living space in urban environments and for improving small-area population estimations. In this study, we assess the potential of fine-spatial-resolution LiDAR measurements (1 m) coupled with automated techniques for building extraction and land-use classification. The study seeks to provide an answer to the question: what level of information extracted from fine-spatial-resolution LiDAR and aerial photographs can be realistically translated into improved small-area population estimation? This question is addressed through a comparative study of up to seven linear models with building count, building area and/or building volume as explanatory variables at one of two land-use levels: single-family dwelling, multi-family dwelling and other types, versus residential and other types. Results show that, while building volume fits more naturally the population figures, it also represents the most challenging variable to measure by automated means. Because of this, a simple model expressed in terms of residential-building counts results in more reliable population estimates.