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Tabular Data - 90 B - 5 Variables, 3 Observations - UNF:6:an86pZutuVGAi15OCCnEtg==
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Tabular Data - 95 B - 5 Variables, 3 Observations - UNF:6:SibwvPj0yHO/XY0lQPVMYA==
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Tabular Data - 2.7 KB - 25 Variables, 36 Observations - UNF:6:8sn6EJGojRJCKyeDDdztEg==
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Adobe PDF - 136.9 KB -
MD5: 31d9aad23d63cf3cec09898b676ea0da
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Dec 15, 2020 - GIScience / Geoinformatics Research Group
Ludwig, Christina; Hecht, Robert; Lautenbach, Sven; Schorcht, Martin; Zipf, Alexander, 2020, "Mapping Public Urban Green Spaces based on OpenStreetMap and Sentinel-2 imagery using Belief Functions: Data and Source Code", https://doi.org/10.11588/data/UYSAA5, heiDATA, V1, UNF:6:+pceldpLQoaQQqPk4/t1VQ== [fileUNF]
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... |
Dec 15, 2020 -
Mapping Public Urban Green Spaces based on OpenStreetMap and Sentinel-2 imagery using Belief Functions: Data and Source Code
Jupyter Notebook - 184.8 KB -
MD5: 99f2ba63e6a7dde837fdec4976df4dad
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Dec 15, 2020 -
Mapping Public Urban Green Spaces based on OpenStreetMap and Sentinel-2 imagery using Belief Functions: Data and Source Code
Jupyter Notebook - 404.1 KB -
MD5: 0a2cf024c3da9737f016a427c1523c29
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Dec 15, 2020 -
Mapping Public Urban Green Spaces based on OpenStreetMap and Sentinel-2 imagery using Belief Functions: Data and Source Code
Jupyter Notebook - 712.0 KB -
MD5: 1d6de57d17c8bf61e872a21031e455cd
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Dec 15, 2020 -
Mapping Public Urban Green Spaces based on OpenStreetMap and Sentinel-2 imagery using Belief Functions: Data and Source Code
Jupyter Notebook - 921.4 KB -
MD5: c96cff3181dd05797f2104b4afb6877f
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Dec 15, 2020 -
Mapping Public Urban Green Spaces based on OpenStreetMap and Sentinel-2 imagery using Belief Functions: Data and Source Code
Adobe PDF - 14.8 KB -
MD5: a9c8d1829dc139b55828beae21b44f98
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