Urban Street Scale Population Estimation Based on Building Information

  Objectives  With the acceleration of urbanization and the rapid expansion of urban population, the population data on the street scale plays an important role in urban economic, social, resource and environmental development. Therefore, estimating population on urban street scales with high-resolution remote sensing images is of great theoretical and practical value for promoting sustainable urban development.

  Methods  This paper uses the building information in remote sensing images and census data to analyze the correlation between street building information and population data, and the city street scale population estimation models based on building information are proposed. The best estimation model for the number and geometric characteristics of buildings and population is established based on the multivariate stepwise regression method and the Akaike information criterion that determine the significant feature variables of the building.

  Results  The Experiments show that the population estimation model proposed in this paper can estimate the population of street scale with high accuracy. On the one hand, the floor data can be directly applied to the estimation model. On the other hand, the floor data is also the basis for classifying street buildings. Dividing street buildings into low-rise, middle-rise and high-rise buildings according to the number of floors can significantly improve the population estimation effect of the model.

  Conclusions  The population estimation model proposed in this paper can estimate the population of street scale with high accuracy. This paper uses street-scale data in administrative divisions, but the estimation range of the proposed model is theoretically also valid in population estimation across street areas, which needs to be verified in subsequent studies.