The World Settlement Footprint (WSF) 2019 is a 10m resolution binary mask outlining the extent of human settlements globally derived by means of 2019 multitemporal Sentinel-1 (S1) and Sentinel-2 (S2) imagery. Based on the hypothesis that settlements generally show a more stable behavior with respect to most land-cover classes, temporal statistics are calculated for both S1- and S2-based indices. In particular, a comprehensive analysis has been performed by exploiting a number of reference building outlines to identify the most suitable set of temporal features (ultimately including 6 from S1 and 25 from S2). Training points for the settlement and non-settlement class are then generated by thresholding specific features, which varies depending on the 30 climate types of the well-established Köppen Geiger scheme. Next, binary classification based on Random Forest is applied and, finally, a dedicated post-processing is performed where ancillary datasets are employed to further reduce omission and commission errors. Here, the whole classification process has been entirely carried out within the Google Earth Engine platform. To assess the high accuracy and reliability of the WSF2019, two independent crowd-sourcing-based validation exercises have been carried out with the support of Google and Mapswipe, respectively, where overall 1M reference labels have been collected based photointerpretation of very high-resolution optical imagery. Starting backwards from the year 2015 - for which the WSF2015 is used as a reference - settlement and non-settlement training samples for the given target year t are iteratively extracted by applying morphological filtering to the settlement mask derived for the year t+1, as well as excluding potentially mislabeled samples by adaptively thresholding the temporal mean NDBI, MNDWI and NDVI. Finally, binary Random Forest classification in performed. To quantitatively assess the high accuracy and reliability of the dataset, an extensive campaign based on crowdsourcing photointerpretation of very high-resolution airborne and satellite historical imagery has been performed with the support of Google. In particular, for the years 1990, 1995, 2000, 2005, 2010 and 2015, ~200K reference cells of 30x30m size distributed over 100 sites around the world have been labelled, hence summing up to overall ~1.2M validation samples. It is worth noting that past Landsat-5/7 availability considerably varies across the world and over time. Independently from the implemented approach, this might then result in a lower quality of the final product where few/no scenes have been collected. Accordingly, to provide the users with a suitable and intuitive measure that accounts for the goodness of the Landsat imagery, we conceived the Input Data Consistency (IDC) score, which ranges from 6 to 1 with: 6) very good; 5) good; 4) fair; 3) moderate; 2) low; 1) very low. The IDC score is available on a yearly basis between 1985 and 2015 and supports a proper interpretation of the WSF evolution product. The WSF evolution and IDC score datasets are organized in 5138 GeoTIFF files (EPSG4326 projection) each one referring to a portion of 2x2 degree size (~222x222km) on the ground. WSF evolution values range between 1985 and 2015 corresponding to the estimated year of settlement detection, whereas 0 is no data. A comprehensive publication with all technical details and accuracy figures is currently being finalized. For the time being, please refer to Marconcini et al,. 2021.

This dataset clc5 (2015) describes the landscape according to the CORINE Land Cover (CLC) nomenclature. These classes contain mainly information about landcover mixed with some aspects of landuse. CLC5 is based on the more detailed German landcover model from 2015 (LBM-DE2015) which uses separate classes for landcover and landuse and attribute-information about percentage of vegetation and sealing. The mimimum unit for an object is 1 ha. For the CLC5 dataset landcover and landuse classes are combined to unique CLC-classes taking into account the percentage of vegetation and sealing , followed by a generalisation process.

This dataset clc5 (2012) describes the landscape according to the CORINE Land Cover (CLC) nomenclature. These classes contain mainly information about landcover mixed with some aspects of landuse. CLC5 is based on the more detailed German landcover model from 2012 (LBM-DE2012) which uses separate classes for landcover and landuse and attribute-information about percentage of vegetation and sealing. The mimimum unit for an object is 1 ha. For the CLC5 dataset landcover and landuse classes are combined to unique CLC-classes taking into account the percentage of vegetation and sealing , followed by a generalisation process.

This dataset clc5 (2018) describes the landscape according to the CORINE Land Cover (CLC) nomenclature. These classes contain mainly information about landcover mixed with some aspects of landuse. CLC5 is based on the more detailed German landcover model from 2018 (LBM-DE2018) which uses separate classes for landcover and landuse and attribute-information about percentage of vegetation and sealing. The mimimum unit for an object is 1 ha. For the CLC5 dataset landcover and landuse classes are combined to unique CLC-classes taking into account the percentage of vegetation and sealing , followed by a generalisation process.

Bauleitpläne und städtebauliche Satzungen im Kreis Herzogtum Lauenburg

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Bauleitpläne der Stadt Lengerich

Die landwirtschaftlichen Nutzungsarten enthalten für alle beantragbaren Flurstücke flächendeckend die landwirtschaftlichen Nutzungen. Aus den Nutzungsarten wird im Rahmen der InVeKoS-Kontrolle die maximal beihilfefähige Fläche ermittelt. Herkunft: Geoprozessierung geeigneter Daten (ATKIS, ALK, ALB, §32 Biotope) und anschließende manuelle Nachbearbeitung und Qualitätssicherung. Maßstabsangabe: Erfassungsmaßstab 1:600 - 1:1.500 Erstellungsdatum: Mai 2006

Bereitstellung von Sentinel 2-Satellitendaten aus dem Open-Forecast Projekt (www.open-forecast.eu). Dieser Datensatz beinhaltet die Produkte curscene, curscene_mask, curveg, curbest und longveg. curscene: Mit MAJA atmosphärenkorrigierte Sentinel-2 Satellitendatan in 10m Auflösung aller Bänder curscene_mask: curscene daten mit maskierung von Wolken und Wolkenschatten als "no data" curveg: Vegetationsindizes in 10m (NDVI, EVI, MCARI, REIP, Tasselecd Cap) curbest: Monatliches Komposit für Datstellung ohne Wolken und fehlende Bildstellen longveg: Durchschschnittlicher NDVI eines Jahres über die Vegetationsperiode

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