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Automatic mapping of national surface water with OpenStreetMap and Sentinel-2 MSI data using deep learning [Research Data]
Automatic mapping of national surface water with OpenStreetMap and Sentinel-2 MSI data using deep learning [Research Data]
Oct 12, 2021
Li, Hao; Zech, Johannes; Ludwig, Christina; Fendrich, Sascha; Shapiro, Aurelie; Schultz, Michael; Zipf, Alexander, 2021, "Automatic mapping of national surface water with OpenStreetMap and Sentinel-2 MSI data using deep learning [Research Data]", https://doi.org/10.11588/data/AAKAF9, heiDATA, V1
DATASET FOR JOURNAL PAPER (https://doi.org/10.1016/j.jag.2021.102571) Large-scale mapping activities can benefit from the vastly increasing availability of earth observation (EO) data, especially when combined with volunteered geographical information (VGI) using machine learning...
Mapping Public Urban Green Spaces based on OpenStreetMap and Sentinel-2 imagery using Belief Functions: Data and Source Code
Mapping Public Urban Green Spaces based on OpenStreetMap and Sentinel-2 imagery using Belief Functions: Data and Source Code
Dec 15, 2020
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...
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