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Status: Bibliographieeintrag

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Verfasst von:Ludwig, Christina [VerfasserIn]   i
 Hecht, Robert [VerfasserIn]   i
 Lautenbach, Sven [VerfasserIn]   i
 Schorcht, Martin [VerfasserIn]   i
 Zipf, Alexander [VerfasserIn]   i
Titel:Mapping public urban green spaces based on OpenStreetMap and Sentinel-2 imagery using belief functions
Verf.angabe:Christina Ludwig, Robert Hecht, Sven Lautenbach, Martin Schorcht and Alexander Zipf
E-Jahr:2021
Jahr:9 April 2021
Umfang:25 S.
Teil:volume:10
 year:2021
 number:4
 elocationid:251
 pages:1-25
 extent:25
Fussnoten:Gesehen am 23.06.2021
Titel Quelle:Enthalten in: International Society for Photogrammetry and Remote SensingISPRS International Journal of Geo-Information
Ort Quelle:Basel : MDPI, 2012
Jahr Quelle:2021
Band/Heft Quelle:10(2021), 4, Artikel-ID 251, Seite 1-25
ISSN Quelle:2220-9964
Abstract: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.
DOI:doi:10.3390/ijgi10040251
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

Volltext ; Verlag: https://doi.org/10.3390/ijgi10040251
 Volltext: https://www.mdpi.com/2220-9964/10/4/251
 DOI: https://doi.org/10.3390/ijgi10040251
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:data fusion
 Dempster-Shafer theory
 land use
 OpenStreetMap
 remote sensing
 urban areas
 volunteered geographic information
K10plus-PPN:1761129929
Verknüpfungen:→ Zeitschrift

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