Investigation on the Relationship between Population Density and Satellite Image Features—a Deep Learning Based Approach

Timely and accurate population statistic data plays an important role in many fields. To illustrate the demographic characteristics, population density is a crucial factor in evaluating population data. With a dynamic regional migration in population, it is a challenging job to evaluate population d...

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Veröffentlicht in:Journal of Geodesy and Geoinformation Science 2022-12, Vol.5 (4), p.50-58
Hauptverfasser: Zhang, Junxiang, Li, Peiran, Zhang, Haoran, Song, Xuan
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Li, Peiran
Zhang, Haoran
Song, Xuan
description Timely and accurate population statistic data plays an important role in many fields. To illustrate the demographic characteristics, population density is a crucial factor in evaluating population data. With a dynamic regional migration in population, it is a challenging job to evaluate population density without a census-based survey. We present the approach to classify satellite images in different magnitudes in population density and execute the comparative experiment to discuss the factors that influence the identification to the images with the deep learning approach. In this paper, we use satellite imagery and community population density data. With convolutional neural networks, we evaluated the performance of CNN on population estimation with satellite images, found the features that are important in population estimation, and then perform the sensitive analysis.
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subjects Artificial neural networks
Deep learning
Image classification
Machine learning
Neural networks
Performance evaluation
Population (statistical)
Population density
population estimation|satellite imagery|convolutional neural network
Satellite imagery
title Investigation on the Relationship between Population Density and Satellite Image Features—a Deep Learning Based Approach
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