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

Grippa, Taïs; Georganos, Stefanos; Zarougui, Soukaina; Bognounou, Pauline; Diboulo, Eric; Forget, Yann; Lennert, Moritz; Vanhuysse, Sabine; Mboga, Nicholus; Wolff, Eléonore

doi:10.3390/ijgi7070246

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

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Department of Geoscience, Environment & Society, Université Libre De Bruxelles (ULB), 1050 Bruxelles, Belgium

Direction Générale des Impôts, Direction du Cadastre, 01 BP 119 Ouagadougou 01, Burkina Faso

Centre de Recherche en Santé de Nouna (CRSN), BP 02 Nouna, Burkina Faso

Author to whom correspondence should be addressed.

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

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

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MDPI and ACS Style

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. https://doi.org/10.3390/ijgi7070246

AMA Style

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 International Journal of Geo-Information. 2018; 7(7):246. https://doi.org/10.3390/ijgi7070246

Chicago/Turabian Style

Grippa, Taïs, Stefanos Georganos, Soukaina Zarougui, Pauline Bognounou, Eric Diboulo, Yann Forget, Moritz Lennert, Sabine Vanhuysse, Nicholus Mboga, and Eléonore Wolff. 2018. "Mapping Urban Land Use at Street Block Level Using OpenStreetMap, Remote Sensing Data, and Spatial Metrics" ISPRS International Journal of Geo-Information 7, no. 7: 246. https://doi.org/10.3390/ijgi7070246

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

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