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Article

Mapping Public Urban Green Spaces Based on OpenStreetMap and Sentinel-2 Imagery Using Belief Functions

1
GIScience Research Group, Institute of Geography, Heidelberg University, Im Neuenheimer Feld 348, 69120 Heidelberg, Germany
2
Leibniz Institute of Ecological Urban and Regional Development, Weberplatz 1, 01217 Dresden, Germany
3
Heidelberg Institute for Geoinformation Technology (HeiGIT) gGmbH, Heidelberg University, Schloss-Wolfsbrunnenweg 33, 69118 Heidelberg, Germany
*
Author to whom correspondence should be addressed.
Academic Editors: Wolfgang Kainz, A. Yair Grinberger, Marco Minghini, Peter Mooney, Levente Juhász and Godwin Yeboah
ISPRS Int. J. Geo-Inf. 2021, 10(4), 251; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040251
Received: 11 December 2020 / Revised: 24 March 2021 / Accepted: 4 April 2021 / Published: 9 April 2021
Public urban green spaces are important for the urban quality of life. Still, comprehensive open data sets on urban green spaces are not available for most cities. As open and globally available data sets, the potential of Sentinel-2 satellite imagery and OpenStreetMap (OSM) data for urban green space mapping is high but limited due to their respective uncertainties. Sentinel-2 imagery cannot distinguish public from private green spaces and its spatial resolution of 10 m fails to capture fine-grained urban structures, while in OSM green spaces are not mapped consistently and with the same level of completeness everywhere. To address these limitations, we propose to fuse these data sets under explicit consideration of their uncertainties. The Sentinel-2 derived Normalized Difference Vegetation Index was fused with OSM data using the Dempster–Shafer theory to enhance the detection of small vegetated areas. The distinction between public and private green spaces was achieved using a Bayesian hierarchical model and OSM data. The analysis was performed based on land use parcels derived from OSM data and tested for the city of Dresden, Germany. The overall accuracy of the final map of public urban green spaces was 95% and was mainly influenced by the uncertainty of the public accessibility model. View Full-Text
Keywords: OpenStreetMap; volunteered geographic information; remote sensing; data fusion; land use; Dempster–Shafer theory; urban areas OpenStreetMap; volunteered geographic information; remote sensing; data fusion; land use; Dempster–Shafer theory; urban areas
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MDPI and ACS Style

Ludwig, C.; Hecht, R.; Lautenbach, S.; Schorcht, M.; Zipf, A. Mapping Public Urban Green Spaces Based on OpenStreetMap and Sentinel-2 Imagery Using Belief Functions. ISPRS Int. J. Geo-Inf. 2021, 10, 251. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040251

AMA Style

Ludwig C, Hecht R, Lautenbach S, Schorcht M, Zipf A. Mapping Public Urban Green Spaces Based on OpenStreetMap and Sentinel-2 Imagery Using Belief Functions. ISPRS International Journal of Geo-Information. 2021; 10(4):251. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040251

Chicago/Turabian Style

Ludwig, Christina; Hecht, Robert; Lautenbach, Sven; Schorcht, Martin; Zipf, Alexander. 2021. "Mapping Public Urban Green Spaces Based on OpenStreetMap and Sentinel-2 Imagery Using Belief Functions" ISPRS Int. J. Geo-Inf. 10, no. 4: 251. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040251

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