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ISPRS Int. J. Geo-Inf. 2018, 7(7), 246; https://doi.org/10.3390/ijgi7070246

Mapping Urban Land Use at Street Block Level Using OpenStreetMap, Remote Sensing Data, and Spatial Metrics

1
Department of Geoscience, Environment & Society, Université Libre De Bruxelles (ULB), 1050 Bruxelles, Belgium
2
Direction Générale des Impôts, Direction du Cadastre, 01 BP 119 Ouagadougou 01, Burkina Faso
3
Centre de Recherche en Santé de Nouna (CRSN), BP 02 Nouna, Burkina Faso
*
Author to whom correspondence should be addressed.
Received: 1 June 2018 / Revised: 18 June 2018 / Accepted: 19 June 2018 / Published: 22 June 2018
(This article belongs to the Special Issue Urban Environment Mapping Using GIS)
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Abstract

Up-to-date and reliable land-use information is essential for a variety of applications such as planning or monitoring of the urban environment. This research presents a workflow for mapping urban land use at the street block level, with a focus on residential use, using very-high resolution satellite imagery and derived land-cover maps as input. We develop a processing chain for the automated creation of street block polygons from OpenStreetMap and ancillary data. Spatial metrics and other street block features are computed, followed by feature selection that reduces the initial datasets by more than 80%, providing a parsimonious, discriminative, and redundancy-free set of features. A random forest (RF) classifier is used for the classification of street blocks, which results in accuracies of 84% and 79% for five and six land-use classes, respectively. We exploit the probabilistic output of RF to identify and relabel blocks that have a high degree of uncertainty. Finally, the thematic precision of the residential blocks is refined according to the proportion of the built-up area. The output data and processing chains are made freely available. The proposed framework is able to process large datasets, given that the cities in the case studies, Dakar and Ouagadougou, cover more than 1000 km2 in total, with a spatial resolution of 0.5 m. View Full-Text
Keywords: land use; street block; spatial metrics; landscape metrics; OpenStreetMap; machine learning; PostGIS; GRASS GIS; random forest land use; street block; spatial metrics; landscape metrics; OpenStreetMap; machine learning; PostGIS; GRASS GIS; random forest
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Grippa, T.; Georganos, S.; Zarougui, S.; Bognounou, P.; Diboulo, E.; Forget, Y.; Lennert, M.; Vanhuysse, S.; Mboga, N.; Wolff, E. Mapping Urban Land Use at Street Block Level Using OpenStreetMap, Remote Sensing Data, and Spatial Metrics. ISPRS Int. J. Geo-Inf. 2018, 7, 246.

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