Filter
  • Zeitbezug (61)
    -
  • Frequenz (109)
  • Kategorien
  • Datenanbieter
  • Art des Zugangs
  • Lizenzen
Alles zurücksetzen

160 Datensätze

Luft- und Raumfahrt

Alles zurücksetzen

Countries, 2016 - Administrative Units - Dataset

This data set contains the administrative boundaries at country level of the world and is based on the geometry from EBM v12.x. of EuroGeographics for the members of Eurogeographics, the Global Administrative Units Layer (2015) from FAO (UN) and geometry from the Turkish National Statistical Office. This dataset consists of 2 feature classes (regions, boundaries) per scale level and there are 6 different scale levels (100K, 1M, 3M, 10M, 20M and 60M). The public data set (1M - 60M) is available under the Download link indicated below. The full data set (100K - 60M) GISCO.CNTR_2016 is available via the EC restricted download link.

Luft- und Raumfahrt
Bereitgestellt durch

European Commission, Eurostat (ESTAT), GISCO

Art des Datenzugangs

Shape / WWW

Aktualität der Datensatzbeschreibung

12.05.2021

Zeitbezug der Daten

01.01.2017 —

Raumbezug

Land Cover Map of Europe 2017 at 10m resolution

The Land Cover Map of Europe 2017 is a product resulting from the Phase 2 of the S2GLC project. The final map has been produced on the CREODIAS platform with algorithms and software developed by CBK PAN. Classification of over 15 000 Sentinel-2 images required high level of automation that was assured by the developed software.

The legend of the resulting Land Cover Map of Europe 2017 consists of 13 land cover classes. The pixel size of the map equals 10 m, which corresponds to the highest spatial resolution of Sentinel-2 imagery. Its overall accuracy was estimated to be at the level of 86% using approximately 52 000 validation samples distributed across Europe.

Related publication: https://doi.org/10.3390/rs12213523

Luft- und Raumfahrt
Bereitgestellt durch

Sentinel-2 Global Land Cover project (S2GLC)

Art des Datenzugangs

WWW

Aktualität der Datensatzbeschreibung

12.05.2021

Zeitbezug der Daten

01.01.2017 — 31.12.2017

Aktualisierungsfrequenz

Unregelmäßig

Raumbezug

Landcover classification map of Germany 2020 based on Sentinel-2 data

This landcover map was produced with a classification method developed in the project incora (Inwertsetzung von Copernicus-Daten für die Raumbeobachtung, mFUND Förderkennzeichen: 19F2079C) in cooperation with ILS (Institut für Landes- und Stadtentwicklungsforschung gGmbH) and BBSR (Bundesinstitut für Bau-, Stadt- und Raumforschung) funded by BMVI (Federal Ministry of Transport and Digital Infrastructure). The goal of incora is an analysis of settlement and infrastructure dynamics in Germany based on Copernicus Sentinel data.

This classification is based on a time-series of monthly averaged, atmospherically corrected Sentinel-2 tiles (MAJA L3A-WASP: https://geoservice.dlr.de/web/maps/sentinel2:l3a:wasp; DLR (2019): Sentinel-2 MSI - Level 2A (MAJA-Tiles)- Germany). It consists of the following landcover classes:
10: forest
20: low vegetation
30: water
40: built-up
50: bare soil
60: agriculture

Potential training and validation areas were automatically extracted using spectral indices and their temporal variability from the Sentinel-2 data itself as well as the following auxiliary datasets:
- OpenStreetMap (Map data copyrighted OpenStreetMap contributors and available from htttps://www.openstreetmap.org)
- Copernicus HRL Imperviousness Status Map 2018 (© European Union, Copernicus Land Monitoring Service 2018, European Environment Agency (EEA))
- S2GLC Land Cover Map of Europe 2017 (Malinowski et al. 2020: Automated Production of Land Cover/Use Map of Europe Based on Sentinel-2 Imagery. Remote Sens. 2020, 12(21), 3523; https://doi.org/10.3390/rs12213523)
- Germany NUTS administrative areas 1:250000 (© GeoBasis-DE / BKG 2020 / dl-de/by-2-0 / https://gdz.bkg.bund.de/index.php/default/nuts-gebiete-1-250-000-stand-31-12-nuts250-31-12.html)
- Contains modified Copernicus Sentinel data (2020), processed by mundialis

Processing was performed for blocks of federal states and individual maps were mosaicked afterwards.
For each class 100,000 pixels from the potential training areas were extracted as training data.

Incora report with details on methods and results: pending

mFUND-Projekt: incora, FKZ: 19F2079C

Luft- und Raumfahrt
Bereitgestellt durch

mundialis GmbH & Co. KG

Art des Datenzugangs

Dateidownload

Aktualität der Datensatzbeschreibung

12.05.2021

Zeitbezug der Daten

01.01.2020 — 31.12.2020

Aktualisierungsfrequenz

Unregelmäßig

Raumbezug

Landcover classification map of Germany 2019 based on Sentinel-2 data

This landcover map was produced as an intermediate result in the course of the project incora (Inwertsetzung von Copernicus-Daten für die Raumbeobachtung, mFUND Förderkennzeichen: 19F2079C) in cooperation with ILS (Institut für Landes- und Stadtentwicklungsforschung gGmbH) and BBSR (Bundesinstitut für Bau-, Stadt- und Raumforschung) funded by BMVI (Federal Ministry of Transport and Digital Infrastructure). The goal of incora is an analysis of settlement and infrastructure dynamics in Germany based on Copernicus Sentinel data.

This classification is based on a time-series of monthly averaged, atmospherically corrected Sentinel-2 tiles (MAJA L3A-WASP: https://geoservice.dlr.de/web/maps/sentinel2:l3a:wasp; DLR (2019): Sentinel-2 MSI - Level 2A (MAJA-Tiles)- Germany). It consists of the following landcover classes:
10: forest
20: low vegetation
30: water
40: built-up
50: bare soil
60: agriculture

Potential training and validation areas were automatically extracted using spectral indices and their temporal variability from the Sentinel-2 data itself as well as the following auxiliary datasets:
- OpenStreetMap (Map data copyrighted OpenStreetMap contributors and available from htttps://www.openstreetmap.org)
- Copernicus HRL Imperviousness Status Map 2018 (© European Union, Copernicus Land Monitoring Service 2018, European Environment Agency (EEA))
- S2GLC Land Cover Map of Europe 2017 (Malinowski et al. 2020: Automated Production of Land Cover/Use Map of Europe Based on Sentinel-2 Imagery. Remote Sens. 2020, 12(21), 3523; https://doi.org/10.3390/rs12213523)
- Germany NUTS administrative areas 1:250000 (© GeoBasis-DE / BKG 2020 / dl-de/by-2-0 / https://gdz.bkg.bund.de/index.php/default/nuts-gebiete-1-250-000-stand-31-12-nuts250-31-12.html)
- Contains modified Copernicus Sentinel data (2019), processed by mundialis

Processing was performed for blocks of federal states and individual maps were mosaicked afterwards.
For each class 100,000 pixels from the potential training areas were extracted as training data.

Incora report with details on methods and results: pending

mFUND-Projekt: incora, FKZ: 19F2079C

Luft- und Raumfahrt
Bereitgestellt durch

mundialis GmbH & Co. KG

Art des Datenzugangs

Dateidownload

Aktualität der Datensatzbeschreibung

12.05.2021

Zeitbezug der Daten

01.01.2019 — 31.12.2019

Aktualisierungsfrequenz

Unregelmäßig

Raumbezug

Change Detection map of Germany 2016-2019 based on Sentinel-2 data

This change map was produced as an intermediate result in the course of the project incora (Inwertsetzung von Copernicus-Daten für die Raumbeobachtung, mFUND Förderkennzeichen: 19F2079C) in cooperation with ILS (Institut für Landes- und Stadtentwicklungsforschung gGmbH) and BBSR (Bundesinstitut für Bau-, Stadt- und Raumforschung) funded by BMVI (Federal Ministry of Transport and Digital Infrastructure). The goal of incora is an analysis of settlement and infrastructure dynamics in Germany based on Copernicus Sentinel data.

The map indicates land cover changes between the years 2016 and 2019. It is a difference map from two classifications based on Sentinel-2 MAJA data (MAJA L3A-WASP: https://geoservice.dlr.de/web/maps/sentinel2:l3a:wasp; DLR (2019): Sentinel-2 MSI - Level 2A (MAJA-Tiles)- Germany). More information on the two basis classifications can be found here:

https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/d93aecdd-2cfc-41fe-accc-d636b8ad4f6a
https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/1da8a111-847d-41ee-91bb-3e9b9f5d278f

To keep only significant changes in the change detection map, the following postprocessing steps are applied to the initial difference raster:
- Modefilter (3x3) to eliminate isolated pixels and edge effects
- Information gain in a 4x4 window compares class distribution within the window from the two timesteps. High values indicate that the class distribution in the window has changed, and thus a change is likely. Gain ranges from 0 to 1, all changes < 0.5 are omitted.
- Change areas < 1ha are removed

The resulting map has the following nomenclature:
0: No Change
1: Change from low vegetation to forest
2: Change from water to forest
3: Change from built-up to forest
4: Change from bare soil to forest
5: Change from agriculture to forest
6: Change from forest to low vegetation
7: Change from water to low vegetation
8: Change from built-up to low vegetation
9: Change from bare soil to low vegetation
10: Change from agriculture to low vegetation
11: Change from forest to water
12: Change from low vegetation to water
13: Change from built-up to water
14: Change from bare soil to water
15: Change from agriculture to water
16: Change from forest to built-up
17: Change from low vegetation to built-up
18: Change from water to built-up
19: Change from bare soil to built-up
20: Change from agriculture to built-up
21: Change from forest to bare soil
22: Change from low vegetation to bare soil
23: Change from water to bare soil
24: Change from built-up to bare soil
25: Change from agriculture to bare soil
26: Change from forest to agriculture
27: Change from low vegetation to agriculture
28: Change from water to agriculture
29: Change from built-up to agriculture
30: Change from bare soil to agriculture

- Contains modified Copernicus Sentinel data (2016/2019), processed by mundialis

Incora report with details on methods and results: pending

mFUND-Projekt: incora, FKZ: 19F2079C

Luft- und Raumfahrt
Bereitgestellt durch

mundialis GmbH & Co. KG

Art des Datenzugangs

Dateidownload

Aktualität der Datensatzbeschreibung

12.05.2021

Zeitbezug der Daten

01.01.2016 — 31.12.2019

Aktualisierungsfrequenz

Unregelmäßig

Raumbezug

Landcover classification map of Germany 2016 based on Sentinel-2 data

This landcover map was produced as an intermediate result in the course of the project incora (Inwertsetzung von Copernicus-Daten für die Raumbeobachtung, mFUND Förderkennzeichen: 19F2079C) in cooperation with ILS (Institut für Landes- und Stadtentwicklungsforschung gGmbH) and BBSR (Bundesinstitut für Bau-, Stadt- und Raumforschung) funded by BMVI (Federal Ministry of Transport and Digital Infrastructure). The goal of incora is an analysis of settlement and infrastructure dynamics in Germany based on Copernicus Sentinel data.

This classification is based on a time-series of monthly averaged, atmospherically corrected Sentinel-2 tiles (MAJA L3A-WASP: https://geoservice.dlr.de/web/maps/sentinel2:l3a:wasp; DLR (2019): Sentinel-2 MSI - Level 2A (MAJA-Tiles)- Germany). It consists of the following landcover classes:
10: forest
20: low vegetation
30: water
40: built-up
50: bare soil
60: agriculture

Potential training and validation areas were automatically extracted using spectral indices and their temporal variability from the Sentinel-2 data itself as well as the following auxiliary datasets:
- OpenStreetMap (Map data copyrighted OpenStreetMap contributors and available from htttps://www.openstreetmap.org)
- Copernicus HRL Imperviousness Status Map 2018 (© European Union, Copernicus Land Monitoring Service 2018, European Environment Agency (EEA))
- S2GLC Land Cover Map of Europe 2017 (Malinowski et al. 2020: Automated Production of Land Cover/Use Map of Europe Based on Sentinel-2 Imagery. Remote Sens. 2020, 12(21), 3523; https://doi.org/10.3390/rs12213523)
- Germany NUTS administrative areas 1:250000 (© GeoBasis-DE / BKG 2020 / dl-de/by-2-0 / https://gdz.bkg.bund.de/index.php/default/nuts-gebiete-1-250-000-stand-31-12-nuts250-31-12.html)
- Contains modified Copernicus Sentinel data (2016), processed by mundialis

Processing was performed for blocks of federal states and individual maps were mosaicked afterwards.
For each class 100,000 pixels from the potential training areas were extracted as training data.

Incora report with details on methods and results: pending

mFUND-Projekt: incora, FKZ: 19F2079C

Luft- und Raumfahrt
Bereitgestellt durch

mundialis GmbH & Co. KG

Art des Datenzugangs

Dateidownload

Aktualität der Datensatzbeschreibung

12.05.2021

Zeitbezug der Daten

01.01.2016 — 31.12.2016

Aktualisierungsfrequenz

Unregelmäßig

Raumbezug

TimeScan - Landsat - 2015, Global

The TimeScan product is based on the fully-automated analysis of comprehensive time-series acquisitions of Landsat data. Based on a user-specified definition of the required period of time, the region of interest and – optionally – the maximum cloud cover, the TimeScan processor starts with the collection of all available Landsat scenes that meet the user specification. Next, for each single scene masking of clouds, haze and shadow is conducted using the Fmask algorithm. Then, a total of 6 indices is calculated for those pixels of each single scene that have not been masked in the prior step. The set of indices includes the Normalized Difference Vegetation Index (NDVI), the Built-up Index (BI), the Modified Normalized Difference Water Index (MNDWI), the Normalized Difference Band-5 / Band-7 (ND57), the Normalized Difference Band-4 / Band-3 (ND43), and the Normalized Difference Band-3 / Band-2 (ND32). Finally, the TimeScan product is generated by calculating the temporal statistics (minimum, maximum, mean, standard deviation, mean slope) for each index over the defined period of time. Hence, in case of the defined 6 indices chosen, the TimeScan product will include a total of 30 bands (5 statistical features per index). As an additional band a quality layer is added which shows for each pixel the number of valid values (meaning times with no cloud/haze or shadow cover) that have been included in the statistics calculation.

Luft- und Raumfahrt
Straßen
Bereitgestellt durch

German Aerospace Center (DLR)

Art des Datenzugangs

WMS

Aktualität der Datensatzbeschreibung

10.05.2021

Zeitbezug der Daten

11.04.2013 — 30.10.2015

Aktualisierungsfrequenz

Unregelmäßig

Raumbezug

Area Management/ Restriction/ Regulation Zones and Reporting Units / Lärmschutzbereiche am Flughafen Berlin Brandenburg

Der interoperable INSPIRE-Datensatz gibt einen Überblick über die Lärmschutzbereiche am Flughafen Berlin Brandenburg (BER). Dies umfasst laut Gesetz zum Schutz gegen Fluglärm (FlugLärmG) die Tag-Schutzzonen 1 und 2 sowie eine Nacht-Schutzzone. Es erfolgte eine Schematransformation in das INSPIRE-Zielschema Bewirtschaftsungsgebiete/Schutzgebiete/geregelte Gebiete und Berichterstattungseinheiten.
Der Datensatz ist Grundlage der interoperablen INSPIRE-Darstellungs- (WMS) und Downloaddienste (WFS):

Lärmschutzbereich am Flughafen Berlin Brandenburg - Interoperabler INSPIRE View-Service (WMS-AM-FLUGLAERMSCHUTZ)
Lärmschutzbereich am Flughafen Berlin Brandenburg - Interoperabler INSPIRE Download-Service (WFS-AM-FLUGLAERMSCHUTZ)

Luft- und Raumfahrt
Bereitgestellt durch

Landesamt für Umwelt Brandenburg (LfU)

Art des Datenzugangs

GML

Aktualität der Datensatzbeschreibung

06.05.2021

Raumbezug

Interoperabler INSPIRE Download-Service: Area Management/Restriction/Regulation Zo-nes and Reporting Units / Lärmschutzbereiche am Flughafen Berlin Brandenburg (WFS-AM-FLUGLAERMSCHUTZ)

Der interoperable INSPIRE-Downloaddienst (WFS) Bewirtschaftungsgebiete/Schutzge-biete/geregelte Gebiete und Berichterstattungseinheiten gibt einen Überblick über die Lärm-schutzbereiche am Flughafen Berlin Brandenburg (BER). Dies umfasst laut Gesetz zum Schutz gegen Fluglärm (FlugLärmG) die Tag-Schutzzonen 1 und 2 sowie eine Nacht-Schutzzone. Gemäß der INSPIRE-Datenspezifikation Area Management/Restriction/Regu-lation Zones and Reporting Units (D2.8.III.11_v3.0) liegen die Inhalte INSPIRE-konform vor. Der WFS beinhaltet den FeatureType Bewirtschaftungsgebiet, Schutzgebiet oder geregel-tes Gebiet (am:ManagementRestrictionOrRegulationZone) mit Angaben zum speziellen Ge-bietstyp (SpecialisedZoneTypeCode).

Straßen
Bahn
Luft- und Raumfahrt
Bereitgestellt durch

Landesamt für Umwelt Brandenburg (LfU)

Art des Datenzugangs

WFS / Unbekannt

Aktualität der Datensatzbeschreibung

06.05.2021

Raumbezug

Interoperabler INSPIRE View-Service: Area Management/Restriction/Regulation Zones and Reporting Units / Lärmschutzbereiche am Flughafen Berlin Brandenburg (WMS-AM-FLUG-LAERMSCHUTZ)

Der interoperable INSPIRE-Darstellungsdienst (WMS) Bewirtschaftungsgebiete/Schutzge-biete/geregelte Gebiete und Berichterstattungseinheiten gibt einen Überblick über die Lärm-schutzbereiche am Flughafen Berlin Brandenburg (BER). Dies umfasst laut Gesetz zum Schutz gegen Fluglärm (FlugLärmG) die Tag-Schutzzonen 1 und 2 sowie eine Nacht-Schutzzone. Gemäß der INSPIRE-Datenspezifikation Area Management/Restriction/Regu-lation Zones and Reporting Units (D2.8.III.11_v3.0) liegen die Inhalte der Karte INSPIRE-konform vor. Der WMS beinhaltet den folgenden Layer:
- AM.NoiseRestrictionZone: Der festgesetzte Lärmschutzbereich mit den Tag-Schutzzonen 1 und 2 sowie einer Nacht-Schutzzone.

Der WebMapService (WMS) wird in den Versionen 1.1.1 und 1.3.0 bereitgestellt.

Straßen
Bahn
Luft- und Raumfahrt
Bereitgestellt durch

Landesamt für Umwelt Brandenburg (LfU)

Art des Datenzugangs

Unbekannt / WMS

Aktualität der Datensatzbeschreibung

06.05.2021

Raumbezug

1 - 10 von 160 Datensätzen

Ergebnisse als CSV oder RDF/XML