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

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 satelli...

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Bibliographic Details
Published in:	ISPRS international journal of geo-information Vol. 7; no. 7; p. 246
Main Authors:	Grippa, Taïs, Georganos, Stefanos, Zarougui, Soukaina, Bognounou, Pauline, Diboulo, Eric, Forget, Yann, Lennert, Moritz, Vanhuysse, Sabine, Mboga, Nicholus, Wolff, Eléonore
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01-07-2018
Subjects:	Case studies Chains Classification Data Data processing Datasets Developing countries Digital mapping Environmental monitoring Frameworks GRASS GIS Image resolution Imagery Land cover Land use Land use management landscape metrics LDCs machine learning Mapping Open systems OpenStreetMap Population density PostGIS random forest Redundancy Remote sensing Residential areas Resolution Satellite imagery Satellites Spaceborne remote sensing Spatial data spatial metrics Spatial resolution street block Studies Urban areas Urban environments Urbanization Workflow United States > US Burkina Faso Africa
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Summary:	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.
ISSN:	2220-9964 2220-9964
DOI:	10.3390/ijgi7070246