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Improving Urban Population Distribution Models with Very-High Resolution Satellite Information

1
Department of Geoscience, Environment & Society, Université Libre De Bruxelles (ULB), 1050 Bruxelles, Belgium
2
Department of Geography, University of Namur, 5000 Namur, Belgium
*
Author to whom correspondence should be addressed.
Received: 5 December 2018 / Revised: 7 January 2019 / Accepted: 14 January 2019 / Published: 16 January 2019
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Abstract

Built-up layers derived from medium resolution (MR) satellite information have proven their contribution to dasymetric mapping, but suffer from important limitations when working at the intra-urban level, mainly due to their difficulty in capturing the whole range of variation in terms of built-up densities. In this regard, very-high resolution (VHR) remote sensing is known for its ability to better capture small variations in built-up densities and to derive detailed urban land use, which plead in favor of its use when mapping urban populations. In this paper, we compare the added value of various combinations of VHR data sets, compared to a MR one. A top-down dasymetric mapping strategy is applied to reallocate population counts from administrative units into a regular 100 × 100 m grid, according to different weighting layers. These weighting layers are created from MR and/or VHR input data, using simple built-up proportion or reallocation “weights”, obtained from a set of multiple ancillary data used to train a Random Forest regression model. The results reveal that (1) a built-up mask derived from VHR can improve the accuracy of the reallocation by roughly 13%, compared to MR; (2) using VHR land-use information alone results in lower accuracy than using a MR built-up mask; and (3) there is a clear complementarity between VHR land cover and land use. View Full-Text
Keywords: population modelling; dasymetric mapping; top-down approach; very-high resolution data; remote sensing; random forest; African city population modelling; dasymetric mapping; top-down approach; very-high resolution data; remote sensing; random forest; African city
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Grippa, T.; Linard, C.; Lennert, M.; Georganos, S.; Mboga, N.; Vanhuysse, S.; Gadiaga, A.; Wolff, E. Improving Urban Population Distribution Models with Very-High Resolution Satellite Information. Data 2019, 4, 13.

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