Noise - Road Traffic Lden (AI Prediction) - Germany, 2017

This dataset includes a road traffic noise estimation using ensemble learning and multimodal geodata. The official noise indicator, Lden (Day-Evening-Night Level), mapped according to the European Noise Directive (2002/49/EC) for large urban agglomerations is extrapolated to suburban areas and beyond. This novel information closes previous data gaps and is herewith available for environmental noise assessments evaluating the impact of road traffic noise on human health and well-being at a spatial resolution of 10 x 10m nation-wide.

Date (Creation): 2025-10-16T10:48:00

Edition: 1.0

Citation identifier: https://geoservice.dlr.de/catalogue/srv/metadata/3eab45fd-5049-4c93-8041-d949bea00b0a

Presentation form: Digital map

Other citation details: DOI: 10.15489/5non57bdli63

Status: Completed

Point of contact

German Aerospace Center (DLR)

Author

German Aerospace Center (DLR) - Jeroen Staab

Author

German Aerospace Center (DLR) - Michael Wurm

Author

German Aerospace Center (DLR) - Hannes Taubenböck

Maintenance and update frequency: As needed

GEMET - INSPIRE themes, version 1.0

Transport networks
Human health and safety

Keywords

Road Traffic
Noise
Exposure
Residential environment
Health
2002/49/EC
Germany

Use limitation: Nutzungseinschränkungen: Das DLR ist nicht haftbar für Schäden, die sich aus der Nutzung ergeben. / Use Limitations: DLR not liable for damage resulting from use.

Access constraints: Other restrictions

Other constraints: License

Use constraints: License

Use constraints: Other restrictions

Other constraints: Nutzungsbedingungen: Lizenz, https://creativecommons.org/licenses/by-nc/4.0 / Terms of use: License, https://creativecommons.org/licenses/by-nc/4.0

Other constraints: {"id": "cc-by/4.0",

"name": "Creative Commons Namensnennung - Nicht kommerziell 4.0 International (CC BY 4.0)",

"url": " http://dcat-ap.de/def/licenses/cc-by/4.0 ",

"quelle": "Copyright DLR (year of production)"}

Spatial representation type: Grid

Denominator: 20000

Language: English

Character set: UTF8

Topic category

Transportation
Environment

Begin date: 2017-01-01T00:00:00

End date: 2017-12-31T00:00:00

Unique resource identifier: EPSG:3035

Number of dimensions: 2

Dimension name: Column

Dimension size: 65120

Resolution: 10 m

Dimension name: Row

Dimension size: 87740

Resolution: 10 m

Cell geometry: Area

Transformation parameter availability: No

Distribution format

GeoTIFF ( )

OnLine resource

N2N_NOISE_AI_DE_2017 ( OGC:WMS )

Noise - Road Traffic Lden (AI Prediction) - Germany, 2017

OnLine resource

EOC Geoservice Map Context ( WWW:LINK-1.0-http--link )

EOC Geoservice Map Context (eoc:n2nnoisea)

OnLine resource

HTTP Download ( WWW:LINK-1.0-http--link )

HTTP Download (Noise2Nako NOISE)

OnLine resource

Noise2NAKO(AI) project website ( WWW:LINK-1.0-http--link )

Website of the Noise2NAKO(AI) project

OnLine resource

Scientific publication ( WWW:LINK-1.0-http--link )

Jeroen Staab, Matthias Weigand, Arthur Schady, Ariane Droin, Donatella Cea, Marco Dallavalle, Nikolaos Nikolaou, Mahyar Valizadeh, Kathrin Wolf, Michael Wurm, Tobia Lakes, Hannes Taubenböck. 2025. National road traffic noise estimation with ensemble learning and multimodal geodata, Transportation Research Part D: Transport and Environment. 149, 105063.

Hierarchy level: Dataset

Domain consistency

Measure identification: INSPIRE / Conformity_001

Conformance result

Date (Publication): 2010-12-08

Explanation: See the referenced specification.

Pass: Yes

Statement: Input data: Selected strategic noise maps reported under under the 2002/49/EC obligations to EEA (from 10.15489/a6wg11lrub77), land-cover information (Land Cover DE - Sentinel-2 - Germany, 2015; https://doi.org/10.15489/1ccmlap3mn39 ), NDVI mosaic based on Copernicus Sentinel-2 (Sentinel-2 - Vegetation Index (NDVI) - Germany, 2015, https://doi.org/10.15489/z5rq0pr8wv8 5)