Population estimation based on multi-sensor data fusion

Abstract

This research examines the utility of QuickBird imagery and Light Detection and Ranging (LiDAR) data for estimating population at the census-block level using two approaches: area-based and volume-based. Residential-building footprints are first delineated from the remote-sensing data using image segmentation and machine-learning decision-tree classification. Regression analysis is used to model the relationship between population and the area or volume of the delineated residential buildings. Both approaches result in successful performance for estimating population with high accuracy (coefficient of determination = 0.8-0.95; root-mean-square error = 10-30 people; relative root-mean-square error = 0.1-0.3). The area-based approach is slightly better than the volume-based approach because the residential areas of the study sites are generally homogeneous (i.e. single houses), and the volume-based approach is more sensitive to classification errors. The LiDAR-derived shape information such as height greatly improves population estimation compared to population estimation using only spectral data.