Population Estimation of Urban Residential Communities Using Remotely Sensed Morphologic Data

Fine-scale population estimation in urban areas provides information useful in such fields as emergency response, epidemiological applications, and urban management. It is however a challenge because of lack of detailed building morphologic information. This research investigated the capability of LiDAR data for extraction of residential buildings and used the results for population estimation in heterogeneous environments in Indianapolis, USA. A morphological building detection algorithm was applied, to extract buildings from LiDAR point cloud, and yielded an overall detection accuracy of 95%. Extracted buildings were then categorized into nonresidential buildings, apartments, single-family houses, and other buildings based on selected geometric features (e.g., area, height, and volume) and background characteristics (vegetation and impervious cover) by a random forest classifier. Linear regression modeling, based on area, volume, and housing units, was applied to examine the relationship between census population and LiDAR-derived residential variables. The results show that morphological metrics extracted from LiDAR can be applied to classify buildings with relatively high accuracy, with an overall accuracy of 81.67%. The shape indexes contributed mostly to the residential building extraction followed by building background metrics. By excluding nonresidential buildings, the accuracy of population estimation increased from an RMSE of 20 and a mean absolute relative error (MARE) of 61.38% to an RMSE of 13 and a MARE of 33.52%. The differentiation between single-family houses and apartments contributed to the improved estimation. Additionally, the introduction of building height resulted in relatively accurate unit-based estimation. This study provides important insights into fine-scale population estimation in heterogeneous urban regions, when detailed building information is unavailable.