d47a2d18-8443-4428-837f-b079d171a7e7 dataset German Aerospace Center (DLR) geoservice@dlr.de 2024-06-20T13:08:04 ISO 19115-1:2014/19139 2 90000 0.1 56000 0.1 false EPSG:3035 UrMo Digital - Traffic Area Map (TAM) - Brunswick, Germany 2022-08-01T00:00:00 https://geoservice.dlr.de/catalogue/srv/metadata/d47a2d18-8443-4428-837f-b079d171a7e7 This inventory of traffic areas in the city of Brunswick, Germany, is based on image sequences acquired during six flight campaigns at different times of the day and year in 2019 and 2020. Each aerial image is segmented by a neural network into the classes (1) Parking area, (2) Road, and (3) Access way, with the latter two classes differing in terms of their primary transportation function (mobility versus access). The individual segmentations are subsequently merged, since in addition to dedicated parking areas, those traffic areas that are regularly used for parking a motorized vehicle (e.g., at the curbside) are also to be classified as such. Furthermore, the multitemporal fusion enhances the robustness and completeness of the traffic area map (TAM). Potential applications include: urban planning, traffic modeling, and parking management. For more information about the project, the reader is referred to: https://elib.dlr.de/191145/1/Hellekes_et_al_2022_Parking_space_inventory_from_above.pdf To provide an inventory of traffic areas in the city of Brunswick, Germany. The resulting map serves applications like urban planning, transport modeling, and traffic management. German Aerospace Center (DLR) geoservice@dlr.de German Aerospace Center (DLR) geoservice@dlr.de Jens Hellekes German Aerospace Center (DLR) Jens.Hellekes@dlr.de https://geoservice.dlr.de/catalogue/srv/api/records/d47a2d18-8443-4428-837f-b079d171a7e7/attachments/urmo_tam_ql.png large_thumbnail https://geoservice.dlr.de/catalogue/srv/api/records/d47a2d18-8443-4428-837f-b079d171a7e7/attachments/urmo_tam_ql_s.png thumbnail DLR EOC Aerial Imagery Image Segmentation Traffic Area Map Parking Space Detection Multitemporal Fusion Brunswick Germany Transport networks GEMET - INSPIRE themes, version 1.0 2008-06-01 local Spatial scope 2019-05-22 Nutzungseinschränkungen: Das DLR ist nicht haftbar für Schäden, die sich aus der Nutzung ergeben. / Use Limitations: DLR not liable for damage resulting from use. Es gelten keine Zugriffsbeschränkungen Nutzungsbedingungen: Lizenz, https://creativecommons.org/licenses/by-nc/4.0 / Terms of use: License, https://creativecommons.org/licenses/by-nc/4.0 {"id": "cc-by-nc-4.0", "name": "Creative Commons Namensnennung - Nicht kommerziell 4.0 International (CC BY-NC 4.0)", "url": "http://dcat-ap.de/def/licenses/cc-by-nc/4.0", "quelle": "Copyright DLR (year of production)"} 200 transportation 10.48 10.57 52.24 52.32 2019-04-25 2020-06-24 Cloud Optimized GeoTIFF https://geoservice.dlr.de/eoc/land/wms? OGC:WMS URMO_TAM_3K_BRUNSWICK WMS Access: inventory of traffic areas in the city of Brunswick, Germany https://geoservice.dlr.de/eoc/imagery/wms? OGC:WMS 3K_MOS_BRUNSWICK WMS Access: aerial imagery mosaic for the city of Brunswick, Germany https://verkehrsforschung.dlr.de/de/projekte/urmo-digital WWW:LINK-1.0-http--link UrMo Digital - Forschen für die städtische Mobilität der Zukunft Webpage with links and description for accessing more information about the project https://download.geoservice.dlr.de/URMO_DIGITAL/files/ WWW:LINK-1.0-http--link HTTP download HTTP download (UrMo Digital) Conformity_001 INSPIRE VERORDNUNG (EG) Nr. 1089/2010 DER KOMMISSION vom 23. November 2010 zur Durchführung der Richtlinie 2007/2/EG des Europäischen Parlaments und des Rates hinsichtlich der Interoperabilität von Geodatensätzen und -diensten 2010-12-08 See the referenced specification true The traffic are map for Brunswick, Germany is based on aerial images acquired with the DLR 3K camera system at varying times of the day and year between 2019 and 2020, covering about 36 km² with a spatial resolution of 0.1 m. UrMo Digital - Traffic Area Map Processing Data: The classification is based on image sequences acquired during six flight campaigns at different times of the day and year in 2019 and 2020. Imagery was acquired with the 3K camera system at 10 cm ground sampling distance Processing: Each aerial image is segmented by a neural network, multi-temporal fusion is used to improve robustness and detect curbside parking areas.