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Luft- und Raumfahrt

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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/db130a09-fc2e-421d-95e2-1575e7c4b45c
https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/36512b46-f3aa-4aa4-8281-7584ec46c813

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

13.10.2021

Zeitbezug der Daten

01.01.2016 — 31.12.2019

Aktualisierungsfrequenz

Unregelmäßig

Raumbezug

World Settlement Footprint (WSF) 2015 - Landsat-8/Sentinel-1 - Global

The World Settlement Footprint WSF 2015 version 2 (WSF2015 v2) is a 10m resolution binary mask outlining the extent of human settlements globally for the year 2015. Specifically, the WSF2015 v2 is a pilot product generated by combining multiple datasets, namely:
• The WSF2015 v1 derived at 10m spatial resolution by means of 2014-2015 multitemporal Landsat-8 and Sentinel-1 imagery (of which ~217K and ~107K scenes have been processed, respectively); https://doi.org/10.1038/s41597-020-00580-5
• The High Resolution Settlement Layer (HRSL) generated by the Connectivity Lab team at Facebook through the employment of 2016 DigitalGlobe VHR satellite imagery and publicly released at 30m spatial resolution for 214 countries; https://arxiv.org/pdf/1712.05839.pdf
• The novel WSF2019 v1 derived at 10m spatial resolution by means of 2019 multitemporal Sentinel-1 and Sentinel-2 imagery (of which ~ 1.2M and ~1.8M scenes have been processed, respectively); https://doi.org/10.1553/giscience2021_01_s33
The WSF2015 v1 demonstrated to be highly accurate, outperforming all similar existing global layers; however, the use of Landsat imagery prevented a proper detection of very small structures, mostly due to their reduced scale. Based on an extensive qualitative assessment, wherever available the HRSL layer shows instead a systematic underestimation of larger settlements, whereas it proves particularly effective in identifying smaller clusters of buildings down to single houses, thanks to the employment of 2016 VHR imagery. The WSF2015v v2 has been then generated by: i) merging the WSF2015 v1 and HRSL (after resampling to 10m resolution and disregarding the population density information attached); and ii) masking the outcome by means of the WSF2019 product, which exhibits even higher detail and accuracy, also thanks to the use of Sentinel-2 data and the proper employment of state-of-the-art ancillary datasets (which allowed, for instance, to effectively mask out all roads globally from motorways to residential).

Luft- und Raumfahrt
Straßen
Bereitgestellt durch

German Aerospace Center (DLR)

Art des Datenzugangs

WWW / Dateidownload / WMS

Aktualität der Datensatzbeschreibung

11.10.2021

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.

An exemplary validation of the classification results was perfomed for the federal state of North Rhine-Westphalia as its open data policy allows for direct access to official data to be used as reference. Rules to convert relevant ATKIS Basis-DLM object classes to the incora nomenclature were defined. Subsequently, 5.000 reference points were randomly sampled and their classification in each case visually examined and, if necessary, revised to obtain a robust reference data set. The comparison of this reference data set with the incora classification yielded the following results:

overall accurary: 88.4%

class: user's accuracy / producer's accurary (number of reference points n)
forest: 95.0% / 93.8% (1410)
low vegetation: 73.4% / 86.5% (844)
water: 98.5% / 92.8% (69)
built-up: 98.9% / 95.8% (983)
bare soil: 23.9% / 82.9% (41)
agriculture: 94.6% / 83.2% (1653)

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

30.09.2021

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.

An exemplary validation of the classification results was perfomed for the federal state of North Rhine-Westphalia as its open data policy allows for direct access to official data to be used as reference. Rules to convert relevant ATKIS Basis-DLM object classes to the incora nomenclature were defined. Subsequently, 5.000 reference points were randomly sampled and their classification in each case visually examined and, if necessary, revised to obtain a robust reference data set. The comparison of this reference data set with the incora classification yielded the following results:

overall accurary: 91.9%

class: user's accuracy / producer's accurary (number of reference points n)
forest: 98.1% / 95.9% (1410)
low vegetation: 76.4% / 91.5% (844)
water: 98.4% / 92.8% (69)
built-up: 99.2% / 97.4% (983)
bare soil: 35.1% / 95.1% (41)
agriculture: 95.9% / 85.3% (1653)

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

30.09.2021

Zeitbezug der Daten

01.01.2019 — 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.

An exemplary validation of the classification results was perfomed for the federal state of North Rhine-Westphalia as its open data policy allows for direct access to official data to be used as reference. Rules to convert relevant ATKIS Basis-DLM object classes to the incora nomenclature were defined. Subsequently, 5.000 reference points were randomly sampled and their classification in each case visually examined and, if necessary, revised to obtain a robust reference data set. The comparison of this reference data set with the incora classification yielded the following results:

overall accurary: 88.4%

class: user's accuracy / producer's accurary (number of reference points n)
forest: 96.7% / 94.3% (1410)
low vegetation: 70.6% / 84.0% (844)
water: 98.5% / 94.2% (69)
built-up: 98.2% / 89.8% (983)
bare soil: 19.7% / 58.5% (41)
agriculture: 91.7% / 85.3% (1653)

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

30.09.2021

Zeitbezug der Daten

01.01.2016 — 31.12.2016

Aktualisierungsfrequenz

Unregelmäßig

Raumbezug

Change Detection map of Germany 2019-2020 based on Sentinel-2 data

This change map was produced on the basis of 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.

The map indicates land cover changes between the years 2019 and 2020. 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/36512b46-f3aa-4aa4-8281-7584ec46c813
https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/9246503f-6adf-460b-a31e-73a649182d07

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 (2019/2020), 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

30.09.2021

Zeitbezug der Daten

01.01.2019 — 31.12.2020

Aktualisierungsfrequenz

Unregelmäßig

Raumbezug

DFS - INSPIRE Air Transport Network

DFS - INSPIRE Air Transport Network

Luft- und Raumfahrt
Bereitgestellt durch

DFS

Art des Datenzugangs

WFS / WMS

Aktualität der Datensatzbeschreibung

30.09.2021

Raumbezug

DFS - INSPIRE Air Transport Network

DFS - INSPIRE Air Transport Network

Luft- und Raumfahrt
Bereitgestellt durch

DFS

Art des Datenzugangs

WFS / WMS

Aktualität der Datensatzbeschreibung

28.09.2021

Raumbezug

Strat. Lärmkarte L_N (Nacht-Index) Flugverkehr 2017 (UA) - [Atom]

Die Strategischen Lärmkarten 2017 geben Auskunft über die Lärmbelastung im Einwirkbereich von Hauptlärmquellen. Sie sind eine Fortführung der Strategischen Lärmkarten 2012. Datenstand Flughafen Schönefeld: 2010, Flughafen Berlin-Tegel: 2015.

Luft- und Raumfahrt
Bereitgestellt durch

OpenData Berlin: Senatsverwaltung für Umwelt, Verkehr und Klimaschutz Berlin

Art des Datenzugangs

HTML / ATOM

Aktualität der Datensatzbeschreibung

27.09.2021

Aktualisierungsfrequenz

Niemals

Raumbezug

Strat. Lärmkarte L_DEN (Tag-Abend-Nacht-Index) Flugverkehr 2017 (UA) - [Atom]

Die Strategischen Lärmkarten 2017 geben Auskunft über die Lärmbelastung im Einwirkbereich von Hauptlärmquellen. Sie sind eine Fortführung der Strategischen Lärmkarten 2012. Datenstand Flughafen Schönefeld: 2010, Flughafen Berlin-Tegel: 2015.

Luft- und Raumfahrt
Bereitgestellt durch

OpenData Berlin: Senatsverwaltung für Umwelt, Verkehr und Klimaschutz Berlin

Art des Datenzugangs

HTML / ATOM

Aktualität der Datensatzbeschreibung

27.09.2021

Aktualisierungsfrequenz

Niemals

Raumbezug

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