Abstract
Building-level population data are of vital importance in disaster management, homeland security, and public health. Remotely sensed data, especially LiDAR data, which allow measures of three-dimensional morphological information, have been shown to be useful for fine-scale population estimations. However, studies using LiDAR data for population estimation have noted a nonstationary relationship between LiDAR-derived morphological indicators and populations due to the unbalanced characteristic of population distribution. In this article, we proposed a framework to estimate population at the building level by integrating POI data, nighttime light (NTL) data, and LiDAR data. Building objects were first derived using LiDAR data and aerial photographs. Then, three categories of building-level features, including geometric features, nighttime light intensity features, and POI features, were, respectively, extracted from LiDAR data, Luojia1-01 NTL data, and POI data. Finally, a well-trained random forest model was built to estimate the population of each individual building. Huangpu District in Shanghai, China, was chosen to validate the proposed method. A comparison between the estimation result and reference data shows that the proposed method achieved a good accuracy with R 2 = 0.65 at the building level and R 2 = 0.79 at the community level. The NTL radiance intensity was found to have a positive relationship with population in residential areas, while a negative relationship was found in office and commercial areas. Our study has shown that by integrating both the three-dimensional morphological information derived from LiDAR data and the human activity information extracted from POI and NTL data, the accuracy of building-level population estimation can be improved.
Highlights
Fine-scale population data are essential in addressing various critical social, political, and environmental issues, such as epidemic control [1] and natural disaster relief [2]
By overlying the normalized digital surface model (nDSM) data, we found that the lowest building height is 3.65 m, the highest building height is 460 m, and the average height is 6.7 m
Our study shows that for building-level population estimation, a positive correlation was found between nighttime light (NTL) radiance intensity and population in residential areas, while a negative correlation was found in commercial areas
Summary
Fine-scale population data are essential in addressing various critical social, political, and environmental issues, such as epidemic control [1] and natural disaster relief [2]. Most of the available population data are concentrated on administrative units, including countries, provinces, census tracts, and blocks. The usefulness of these data is limited in urban planning, disaster management, fertility policy, climate policy, and public health [3,4,5,6,7] due to the low spatial resolution. The increasing availability and quality of socioeconomic data and three-dimensional morphological data have the potential to improve the fine-scale population estimations, especially at the building level. Studies have turned to remote-sensing techniques which conduct simultaneous observation over large
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have