Mesh-Wise Prediction of Demographic Composition from Satellite Images
  Using Multi-Head Convolutional Neural Network
Yuta Sato
Population aging is one of the most serious problems in certain countries. In
order to implement its countermeasures, understanding its rapid progress is of
urgency with a granular resolution. However, a detailed and rigorous survey
with high frequency is not feasible due to the constraints of financial and
human resources. Nowadays, Deep Learning is prevalent for pattern recognition
with significant accuracy, with its application to remote sensing. This paper
proposes a multi-head Convolutional Neural Network model with transfer learning
from pre-trained ResNet50 for estimating mesh-wise demographics of Japan as one
of the most aged countries in the world, with satellite images from
Landsat-8/OLI and Suomi NPP/VIIRS-DNS as inputs and census demographics as
labels. The trained model was performed on a testing dataset with a test score
of at least 0.8914 in $\text{R}^2$ for all the demographic composition groups,
and the estimated demographic composition was generated and visualised for 2022
as a non-census year.