Convolutional neural networks for disaggregated population mapping using open data

Abstract : High resolution population count data are vital for numerous applications such as urban planning, transportation model calibration, and population growth impact measurements, among others. In this work, we present and evaluate an end-to-end framework for computing disaggregated population mapping employing convolutional neural networks (CNNs). Using urban data extracted from the OpenStreetMap database, a set of urban features are generated which are used to guide population density estimates at a higher resolution. A population density grid at a 200 by 200 meter spatial resolution is estimated, using as input gridded population data of 1 by 1 kilometer. Our approach relies solely on open data with a wide geographical coverage, ensuring replicability and potential applicability to a great number of cities in the world. Fine-grained gridded population data is used for 15 French cities in order to train and validate our model. A stand-alone city is kept out for the validation procedure. The results demonstrate that the neural network approach using massive OpenStreetMap data outperforms other approaches proposed in related works.
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Conference papers
IEEE International Conference on Data Science and Advanced Analytics (DSAA), Oct 2018, Turin, Italy. 2018, 〈https://dsaa2018.isi.it〉
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Luciano Gervasoni, Serge Fenet, Régis Perrier, Peter Sturm. Convolutional neural networks for disaggregated population mapping using open data. IEEE International Conference on Data Science and Advanced Analytics (DSAA), Oct 2018, Turin, Italy. 2018, 〈https://dsaa2018.isi.it〉. 〈hal-01852585〉

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