16 datasets found
  1. g

    Input and output data for the automated development of building floor plans...

    • gimi9.com
    Updated Jul 8, 2024
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    (2024). Input and output data for the automated development of building floor plans for map services and city models | gimi9.com [Dataset]. https://gimi9.com/dataset/eu_620975229629235200
    Explore at:
    Dataset updated
    Jul 8, 2024
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The offer includes data from the mFUND project "LevelOut - Automated development of building floor plans for map services and city models": Input data from digital building models in IFC format and output data for map services and city models, such as CityGML, IndoorGMl and OSM. In the project, digital building models of publicly accessible buildings were prepared in such a way that their interior data can be automatically extracted and integrated into data sets of the urban outdoor space. The generated data can thus serve as a basis for navigation applications for people and autonomous objects. On this basis, innovative applications can improve accessibility in public spaces, increase the attractiveness of rail and public transport, make transport and work processes safer and more efficient, and enable autonomous navigation with AI. -- This offer contains data from the mFUND project LevelOut: input data of digital building models in IFC format and output data for map services and city models, such as CityGML, IndoorGMl and OSM. In the project, digital building models of publicly accessible buildings were processed in such a way that their indoor data can be automatically extracted and be integrated into data sets of the urban outdoor space. The generated data can then be used as a basis for navigation applications for people and autonomous objects. On this basis, innovative applications can contribute to improved accessibility in public spaces, increase attractiveness of rail and public transport, make transportation and work processes safer and more efficient and enable autonomous navigation with AI.

  2. e

    City Information Model of the City of Turku

    • data.europa.eu
    unknown
    Updated Dec 12, 2024
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    Turku (2024). City Information Model of the City of Turku [Dataset]. https://data.europa.eu/data/datasets/fa04edfd-c429-4d0a-9868-ab04f5919d04
    Explore at:
    unknownAvailable download formats
    Dataset updated
    Dec 12, 2024
    Dataset authored and provided by
    Turku
    Area covered
    Turku
    Description

    The City of Turku offers a three-dimensional city data model as open data. The Turku city data model is an intelligent three-dimensional model that consists of both register data and geometry data. The urban information model is part of a process-based approach and its content is produced in different service areas of the city. The model contains various materials from the urban environment of the current situation, from buildings to land use, as well as to terrestrial and underground infrastructure.

    The material can be downloaded freely by anyone.

    Further information:

    Updates:

    • 29.4.2021 Added 3D Auraja laser scanning
    • 9.3.2021 Added WFS Interface (CityGML)
  3. e

    3D Buildings in Malmö city – LOD1

    • data.europa.eu
    Updated Aug 3, 2024
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    (2024). 3D Buildings in Malmö city – LOD1 [Dataset]. https://data.europa.eu/data/datasets/aa8b5c44-1f2b-4c22-9fa7-5849950b41a1/embed
    Explore at:
    Dataset updated
    Aug 3, 2024
    Area covered
    Malmö
    Description

    The overview map buildings raised to 3D, such as CityGML level LOD1 (as blocks/blocks). The data covers the entire area of Malmö municipality. The height of the buildings is set to eaves.

  4. t

    3D building model LoD2-DE Hamburg - Vdataset - LDM

    • service.tib.eu
    Updated Feb 4, 2025
    + more versions
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    (2025). 3D building model LoD2-DE Hamburg - Vdataset - LDM [Dataset]. https://service.tib.eu/ldmservice/dataset/govdata_2c1f2eec-cf9f-4d8b-acac-79d8c1334d5e--2
    Explore at:
    Dataset updated
    Feb 4, 2025
    Area covered
    Hamburg
    Description

    3D building model LoD2-EN For the building model LoD2-DE dataset, standardised roof shapes are formed from point clouds (Airborne laser scanning or photogrammetry) fully automated, assigned to the buildings and aligned according to the actual ridge course. The building floor plan is basically taken from the official digital property map and the model is therefore floor plan compliant.The location accuracy corresponds to that of the underlying building floor plan. The height accuracy is approx. ± 1 m. Coarse deviations are possible in individual cases with complex roof shapes. Shared geometry is managed redundantly. The buildings are additionally blended with terrain information of the Digital Terrain Model (DGM) held at the state operation. There is no manual post-processing of the individual models. The modelling corresponds to the AdV product and quality standard for 3D building models. The timeliness of the data base is usually older from the previous year, in the case of ALS point clouds. (Example: The download LoD2-DE 2023 is based on ALKIS floor plans and point clouds from 2022) The building model LoD2-DE is reserved for the entire urban area of Hamburg (about 750 km²), including the island of Neuwerk. The data can be downloaded as a complete record in CityGML V.1.0 format. Further data formats and excerpts can be obtained at 3d-info@gv.hamburg.de.3D building model LoD2-EN For the building model LoD2-DE dataset, standardised roof shapes are formed from point clouds (Airborne laser scanning or photogrammetry) fully automated, assigned to the buildings and aligned according to the actual ridge course. The building floor plan is basically taken from the official digital property map and the model is therefore floor plan compliant. The location accuracy corresponds to that of the underlying building floor plan. The height accuracy is approx. ± 1 m. Coarse deviations are possible in individual cases with complex roof shapes. Shared geometry is managed redundantly. The buildings are additionally blended with terrain information of the Digital Terrain Model (DGM) held at the state operation. There is no manual post-processing of the individual models.The modelling corresponds to the AdV product and quality standard for 3D building models. The timeliness of the data base is usually older from the previous year, in the case of ALS point clouds.(Example: The download LoD2-DE 2023 is based on ALKIS floor plans and point clouds from 2022) The building model LoD2-DE is reserved for the entire urban area of Hamburg (about 750 km²), including the island of Neuwerk. The data can be downloaded as a complete record in CityGML V.1.0 format. Further data formats and excerpts can be obtained at 3d-info@gv.hamburg.de.

  5. Tokyo23ku LOD2 Building

    • arc-gis-hub-home-arcgishub.hub.arcgis.com
    • hub.arcgis.com
    Updated Apr 7, 2021
    + more versions
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    ESRIジャパン株式会社 (2021). Tokyo23ku LOD2 Building [Dataset]. https://arc-gis-hub-home-arcgishub.hub.arcgis.com/maps/9d1a43732ea54e27998a567a70aafc20
    Explore at:
    Dataset updated
    Apr 7, 2021
    Dataset provided by
    ESRIhttp://esri.com/
    Authors
    ESRIジャパン株式会社
    Area covered
    Description

    「3D都市モデル(Project PLATEAU)東京都23区(CityGML 2020年度)」の東京23区の建物(LOD2、CityGML形式)をArcGIS Pro に取り込み、Webシーンレイヤーとして公開したものです。※データが存在するのは、2021年4月現在で下記サイトにてLOD2データが公開されているエリアです。【3D都市モデル(Project PLATEAU)東京都23区(CityGML 2020年度)のページ】 https://www.geospatial.jp/ckan/dataset/plateau-tokyo23ku-citygml-2020【シーンレイヤーとは】 3D地理空間データをWeb表示に最適化した状態で公開したWebサービスで、大容量の3Dデータを高速に表示することができる。【シーンレイヤーを閲覧可能なソフトウェア】 ・ArcGIS Online Webシーンビューアおよび3D対応Webアプリ ・ArcGIS Pro ・ArcGIS Earth(無償3Dビューアー)

  6. p

    Extending Indoor Open Street Mapping Environments to Navigable 3D CityGML...

    • dona.pwr.edu.pl
    Updated 2018
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    Fodil Fadli; N Kutty; Z Wang; S Zlatanova; Lamine Mahdjoubi; Paweł Bogusławski (2018). Extending Indoor Open Street Mapping Environments to Navigable 3D CityGML Building Models: Emergency Response Assesment / [Dataset]. http://doi.org/10.5194/isprs-archives-XLII-4-161-20
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    Dataset updated
    2018
    Authors
    Fodil Fadli; N Kutty; Z Wang; S Zlatanova; Lamine Mahdjoubi; Paweł Bogusławski
    Description

    Library of Wroclaw University of Science and Technology scientific output (DONA database)

  7. 3D-Gebäudemodell München

    • gis-team-qualitas-esri-training.opendata.arcgis.com
    • hub.arcgis.com
    • +2more
    Updated Jan 19, 2023
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    Esri Deutschland (2023). 3D-Gebäudemodell München [Dataset]. https://gis-team-qualitas-esri-training.opendata.arcgis.com/maps/afce63c0ee9a4a33b2c4ebd29a8e71ef
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    Dataset updated
    Jan 19, 2023
    Dataset provided by
    ESRIhttp://esri.com/
    Authors
    Esri Deutschland
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Description

    Das LoD2 (Level of Detail 2) entspricht der zweiten Ausbaustufe der 3D-Gebäudemodelle. Es handelt sich hierbei um Gebäudemodelle mit ALKIS®-konformen Standarddachformen und beschreibenden Attributen. Als Grundlage für die Modellierung dienen die Gebäudegrundrisse aus ALKIS® und Dächer aus Airborne-Laserscanning-Daten, ALKIS®-3D Gebäudeeinmessung sowie dem luftbildbasierten Digitalen Oberflächenmodell.Quelle: Bayerische VermessungsverwaltungVerarbeitungsprozesse: CityGML Dateien wurden in ArGIS Pro importiert und verarbeitet, nach WGS84 projiziert, als Layer Paket in ArcGIS Online veröffentlicht und daraus ein Scene Layer generiert.

  8. e

    Helsingin 3D-kaupunkimallit

    • data.europa.eu
    • opendata.fi
    • +4more
    unknown
    Updated Apr 20, 2025
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    Helsingin kaupunginkanslia (2025). Helsingin 3D-kaupunkimallit [Dataset]. https://data.europa.eu/data/datasets/02656e14-c13e-4fa7-b51f-ca454621489e?locale=hu
    Explore at:
    unknownAvailable download formats
    Dataset updated
    Apr 20, 2025
    Dataset authored and provided by
    Helsingin kaupunginkanslia
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Helsinki
    Description

    There are two next generation 3D city models of Helsinki available: a semantic city information model and a visually high-quality reality mesh model. You can view the 3D city model and download its information at http://kartta.hel.fi/3d/ . You can find more information here https://www.hel.fi/helsinki/en/administration/information/general/3d/ .

    The city information model

    The city information model allows users to perform a variety of analyses focusing on energy consumption, greenhouse gases or the environmental impacts of traffic, for example.

    The city information model includes a terrain model and buildings. Buildings are presented in two formats: flat-roofed (LoD1) and with differentiated roof structures (LoD2). The LoD2 buildings are also available textured. The buildings are all semantic CityGML objects. Each building has its own identifier (GMLID, RATU and VTJ-PRT) in the database, allowing data streams to be combined. The model uses the ETRS-GK25 plane coordinate system and the N2000 height system. The accuracy of the city information model corresponds to the accuracy of the city plan base map, meaning that buildings are located exactly where they are in the city plan’s base map.

    The city information model

    • licensed under CC BY 4.0
    • online service platform Cesium
    • database postgreSQL / postGIS
    • database schema 3DCityDB
    • data model CityGML 2.0

    You can choose the area that you wish to download data for in the data download service. You can also choose your preferred file format and whether to download textured or untextured data. Building property data is not included.

    Go to the download service https://kartta.hel.fi/3d/

    You can also download the data directly as CityGML files, which are divided into map sheets covering four square kilometres each. The files include building property data.

    Go to CityGML data http://3d.hel.ninja/data/citygml/

    Additionally, you can download the data in WFS API service. The WFS API address is: https://kartta.hel.fi/3d/citydb-wfs/wfs. Make sure to use version 2.0.0.

    Helsinki Energy and Climate Atlas

    The Helsinki Energy and Climate Atlas is a service produced with the city's 3D city information model, which can be found at https://kartta.hel.fi/3d/atlas. A more detailed description of the Helsinki Energy and Climate Atlas and the data sets it contains can be found here. Some of Atlas' source data can also be downloaded from the HRI service. The data can be used to support energy efficiency improvement, energy consumption minimization and renewable energy. The data contained in Atlas on the basic and energy information of buildings has been compiled into an excel file, the description texts of which can be found here (in Finnish). The data is from 2017. The measured district heating, electricity and water consumption data for HEKA buildings for 2015, 2016, 2017 and 2018 can also be downloaded from this page. In particular, in order to support the energy efficiency of the building stock, estimates of the heating energy consumption of almost the entire Helsinki building stock for 2020, 2025, 2030, 2035, 2040, 2045 and 2050 can be downloaded.

    Kalasatama Digital Twins Pilot Project’s Final Report

    The general objective of the KIRA-digi pilot project (15.5.2018 - 31.1.2019) was to produce digital twins of the Kalasatama area. The models serve as a platform for designing, testing, applying and servicing the entire lifecycle of the built environment, as well as smart city development. The progress of the project was divided into five intermediate objectives. The general objective of producing the models was the first. The sharing of 3D city models as open data was the second objective in the project. The third objective was focused on cooperation with the main partner, the Smart Kalasatama project. In the project, an online platform for activities in Kalasatama and interaction with the residents was built on the 3D model platform. The fourth objective of the project was to try out the latest ways to model, test and utilize 3D city models. This intermediate objective applies the basic idea of digital twins: “Design, test and build first digitally.” The fifth objective was to promote the exploitation of digital twins in city processes and service production. An accurate, up-to-date model of an existing city structure and the future plans will enable the development of processes, practices and services based on 3D technology.

    Kalasatama Digital Twins - Final Report of the KIRA-digi Pilot Project (pdf)

    Reality mesh of entire Helsinki (2017)​

    The reality mesh model is a photorealistic city model that, according to its name, it is a

  9. Data from: Texture2LoD3: Enabling LoD3 Building Reconstruction With...

    • zenodo.org
    zip
    Updated Apr 10, 2025
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    Wenzhao Tang; Weihang Li; Olaf Wysocki; Wenzhao Tang; Weihang Li; Olaf Wysocki (2025). Texture2LoD3: Enabling LoD3 Building Reconstruction With Panoramic Images [Dataset]. http://doi.org/10.48550/arxiv.2504.05249
    Explore at:
    zipAvailable download formats
    Dataset updated
    Apr 10, 2025
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Wenzhao Tang; Weihang Li; Olaf Wysocki; Wenzhao Tang; Weihang Li; Olaf Wysocki
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This dataset is designed to support high-fidelity 3D reconstruction of urban facades (LoD3) by integrating geometric, semantic, and texture information. Its structure is as follows:

    • citygml & citygml_buildings.geojson: Provide spatial information, including city-wide building footprints and associated metadata.
    • obj: Contains semantically segmented WallSurfaces, which serve as the basis for further texturing tasks.
    • panoramas: Comprises panorama images sourced from Google Street View, offering rich visual context for texture rectification and mapping.
    • gt_masks: Offers ground truth masks of LoD3 facades, which are used for training and evaluating segmentation approaches. They can also be obtained by our open-source tool: LoD3Masks
    • textures: Consists of facade textures extracted using our Texture2LoD3 method from panoramic images, ensuring high-quality surface visual fidelity for 3D models.

    By combining multiple modalities, this dataset provides a comprehensive foundation for urban facade modeling, texture mapping, and related research. This dataset also contributes to the TUM2TWIN project.

  10. 3D-Gebäudemodell Berlin

    • hub.arcgis.com
    • arc-gis-hub-home-arcgishub.hub.arcgis.com
    • +5more
    Updated Jun 30, 2023
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    Esri Deutschland (2023). 3D-Gebäudemodell Berlin [Dataset]. https://hub.arcgis.com/maps/50e8049abb5841dcb3c113210b2109fb
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    Dataset updated
    Jun 30, 2023
    Dataset provided by
    ESRIhttp://esri.com/
    Authors
    Esri Deutschland
    Area covered
    Description

    3D-Gebäudemodell der Stadt Berlin."Der Datensatz enthält flächendeckend die dreidimensionalen Gebäudemodelle des Landes Berlin im Level of Detail 2 (LoD2). Die Grundrisse der Gebäudemodelle entsprechen genau den Gebäudeumringen, wie sie im Liegenschaftskataster nachgewiesen sind. Die Dachform eines Gebäudemodells entspricht einer generalisierten Standarddachform."Quelle: Geoportal BerlinVerarbeitungsprozesse: CityGML Dateien wurden mit ArcGIS Pro als Multipatch in einer FGDB importiert, nach Web Mercator projiziert und in ArcGIS Online veröffentlicht.

  11. s

    3DCityDB-Web-Map-Client

    • catalog.savenow.de
    html
    Updated Apr 10, 2024
    + more versions
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    Lehrstuhl für Geoinformatik (2024). 3DCityDB-Web-Map-Client [Dataset]. https://catalog.savenow.de/dataset/3dcitydb-web-map-client
    Explore at:
    htmlAvailable download formats
    Dataset updated
    Apr 10, 2024
    Dataset provided by
    Lehrstuhl für Geoinformatik
    Description

    https://www.3dcitydb.org/3dcitydb/wp-content/uploads/2018/09/logo.jpg" alt="l3DCity DB Logo">

    Übersicht

    Der 3DCityDB-Web-Map-Client ist eine Webanwendung zur 3D-Visualisierung und interaktiven Erkundung beliebig großer semantischer 3D-Stadtmodelle. Er dient damit u.a. als Front-End für die 3DCityDB. Der 3DCityDB-Web-Map-Client wurde auf der Grundlage des Cesium Virtual Globe entwickelt, einer Open-Source-JavaScript-Bibliothek, die von Analytical Graphics, Inc (AGI). entwickelt wurde. Er nutzt HTML5 und die Web Graphics Library (WebGL) als Kern für die Hardwarebeschleunigung und bietet plattformübergreifende Funktionalitäten wie die Anzeige von 3D-Grafikinhalten im Web ohne die Notwendigkeit zusätzlicher Plugins.

    Während der Entwicklung des 3DCityDB-Web-Map-Clients wurden verschiedene Erweiterungen für den Cesium Virtual Globe vorgenommen, um es den Benutzern zu erleichtern, 3D-Stadtmodelle bequem zu betrachten und zu erkunden. Die wichtigste dieser Erweiterungen ist, dass die mit dem Import/Export-Werkzeug exportierten KML/glTF-Modelle nun direkt zusammen mit Bild- und Geländeschichten in einem Web-Browser mit dem 3DCityDB-Web-Map-Client visualisiert werden können. Zusätzlich können die KML/glTF-Modelle mit Tabellendaten verknüpfen werden, welche mit dem Spreadsheet Generator Plugin (SPSHG) exportiert wurden. Der 3D-CityDb-Web_Map-Client erlaubt es ebenfalls die thematischen Daten jedes Stadtobjekts direkt abzufragen. In den Online-Demos können Sie sich die neuen Funktionen selbst ansehen.

    Der 3DCityDB-Web-Map-Client ist ebenfalls mit einer Erweiterung ausgestattet, die eine bessere Unterstützung für mobile Geräte bietet. Die Erweiterung verfügt über einen eingebauten mobilen Detektor, der das Verhalten des Clients automatisch erkennen und entsprechend anpassen kann, je nachdem, ob der 3DCityDB-Web-Map-Client auf einem mobilen Gerät betrieben wird. Einige der wichtigsten mobilen Funktionen, die durch diese Erweiterung ermöglicht werden, sowie deren Verwendung werden auf der GitHub-Seite des Tools beschrieben.

    Beispielbilder

    https://raw.githubusercontent.com/3dcitydb/3dcitydb-web-map/master/theme/img/vorarlberg_buildings_geometry_demo.jpg" alt="B3" width="800px">

    Beispiel 1: Visualisierung eines semantischen 3D Landschaftsmodells von Vorarlberg

    https://raw.githubusercontent.com/3dcitydb/3dcitydb-web-map/master/theme/img/railway_scene_lod3_Demo.png" alt="B3" width="800px">

    Besipiel 2: Visualisierung verschiedener LoD3 CityGML-Top-Level-Features (TINRelief, Gebäude, Brücke, Tunnel, Gewässer, Vegetation, Stadtmobiliar, Transport usw.) im glTF-Format

  12. 3D-Gebäudemodell - Frankfurt am Main

    • portal-esri-de.opendata.arcgis.com
    • arc-gis-hub-home-arcgishub.hub.arcgis.com
    • +2more
    Updated Jul 5, 2023
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    Esri Deutschland (2023). 3D-Gebäudemodell - Frankfurt am Main [Dataset]. https://portal-esri-de.opendata.arcgis.com/maps/342b434232f84825852905f88d9786bb
    Explore at:
    Dataset updated
    Jul 5, 2023
    Dataset provided by
    ESRIhttp://esri.com/
    Authors
    Esri Deutschland
    Area covered
    Description

    3D-Gebäudemodell der Stadt Frankfurt am Main."3D-Gebäudemodelle basieren in Hessen auf den Gebäudeumringen aus ALKIS®. Auch Bauwerke, vorwiegend aus dem ATKIS®, finden ergänzend Berücksichtigung im 3D-Gebäudemodell. Nicht berücksichtigt werden unterirdische Gebäude. Die Höheninformation wird durch Verschneiden mit den 3D-Geobasisdaten (Laserdaten, DGM1, bDOM) gewonnen. Das 3D-Gebäudemodell Level of Detail 2 (LoD2) enthält die Zuordnung von standardisierten und generalisierten Dachformen, die entsprechend dem tatsächlichen Firstverlauf und der individuellen Dachneigung ausgerichtet werden.Die Lagegenauigkeit entspricht der Lagegenauigkeit des zugrundeliegenden ALKIS®-/ATKIS®-Objektes.Die Höhengenauigkeit beträgt ca. 1 m. Grobe Abweichungen sind bei komplexen und nicht erkannten Dachformen möglich."Quelle: Hessische Verwaltung für Bodenmanagement und GeoinformationVerarbeitungsprozesse: CityGML Dateien wurden in ArGIS Pro verarbeitet, nach WGS84 projiziert, als Layer Paket in ArcGIS Online veröffentlicht und daraus ein Scene Layer generiert.

  13. a

    3D-model Stad Antwerpen

    • portaal-stadantwerpen.opendata.arcgis.com
    • hub.arcgis.com
    Updated Aug 27, 2019
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    Stad Antwerpen (2019). 3D-model Stad Antwerpen [Dataset]. https://portaal-stadantwerpen.opendata.arcgis.com/documents/d582d2a30593482a99d7505543d7070a
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    Dataset updated
    Aug 27, 2019
    Dataset authored and provided by
    Stad Antwerpen
    Area covered
    Antwerp
    Description

    Het 3D-model van Stad AntwerpenHet 3D-model van Stad Antwerpen werd aangemaakt voor intern gebruik door de Stadsdiensten, ondermeer voor stedebouw- en milieukundige doeleinden. Zoals het berekenen van zonnepotieel van de daken. Via deze site kunnen burgers dit model raadplegen en downloaden.De XY-coördinaten zijn steeds uitgedrukt in Belgisch Lambert 1972 en hoogte is uitgedrukt in Meter TAW, dit is laagwater in Oostende. Het grondvlak is dus niet nul, maar de hoogte van de ondergrond op die plek ten opzichte van zeeniveau.Aanmaak: Het model werd aangemaakt door Eurosense in opdracht van Stad Antwerpen. De brondata is gebasseerd op het Digitaal Hoogtemodel Vlaanderen van 2014 en de GRB-gebouwen, nieuwere gebouwen in de stad hebben dus geen hoogte.RaadplegenJe kan het 3D-model hier bekijken in deze viewer: Naar de viewerDownloadenJe kan ook het 3D-model ook downloaden in tiles van 1 km².Van het skp en glb-formaat worden ook de terreinhoogtes meegegeven.GIS-gebruikers downloaden hiervoor best de data zelf van de site van de Vlaamse overheid.Er worden 4 formaten aangeboden per zip-file:KeyHole Markup language (.kmz): voor google-toepassingenHet Graphics Layer Transmission Format (.glb): gebruikt in 3D animatie en gaming software ondermeer sketchFab en BlenderHet OGC CityGML Formaat (.gml): gebruikt in GIS-toepassingen.Het Sketchup Formaat (.skp): voor het programma sketchup. Naar de download-pagina

  14. 3D-Gebäudemodell Hamburg

    • opendataportal-esri-konferenz-esri-training.opendata.arcgis.com
    • portal-esri-de.opendata.arcgis.com
    • +1more
    Updated May 16, 2022
    + more versions
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    Esri Deutschland (2022). 3D-Gebäudemodell Hamburg [Dataset]. https://opendataportal-esri-konferenz-esri-training.opendata.arcgis.com/maps/943049b99a9d45a1a0b526939c9b9b38
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    Dataset updated
    May 16, 2022
    Dataset provided by
    ESRIhttp://esri.com/
    Authors
    Esri Deutschland
    Area covered
    Description

    3D-Gebäudemodell der Stadt Hamburg."Der Gebäudegrundriss wird grundsätzlich der amtlichen digitalen Liegenschaftskarte entnommen. Den Gebäuden werden standardisierte Dachformen zugeordnet und entsprechend dem tatsächlichen Firstverlauf ausgerichtet. Die Lagegenauigkeit entspricht der des zugrunde liegenden Gebäudegrundrisses. Die Höhengenauigkeit beträgt ca. 1 m. Grobe Abweichungen sind in Einzelfällen bei komplexen Dachformen möglich."Quelle: Transparenzportal HamburgVerarbeitungsprozesse: CityGML Dateien wurden in ArGIS Pro verarbeitet, nach Web Mercator projiziert, als Layer Paket in ArcGIS Online veröffentlicht und daraus ein Scene Layer generiert.

  15. 3D-Gebäudemodell - Potsdam

    • hub.arcgis.com
    • portal-esri-de.opendata.arcgis.com
    • +1more
    Updated Feb 7, 2020
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    Esri Deutschland (2020). 3D-Gebäudemodell - Potsdam [Dataset]. https://hub.arcgis.com/maps/1fa79a8d0f474dbf8c88f1089fb93e97
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    Dataset updated
    Feb 7, 2020
    Dataset provided by
    ESRIhttp://esri.com/
    Authors
    Esri Deutschland
    Area covered
    Description

    3D-Gebäudemodell der Stadt Potsdam.Die Layer wurde aus LOD2 Daten der Landesvermessung und Geobasisinformation Brandenburg generiert.Quelle: geobasis-bb.deVerarbeitungsprozesse: CityGML Dateien wurden in ArGIS Pro verarbeitet (Multipatch), nach WebMercator projiziert, als Layer Paket in ArcGIS Online veröffentlicht und daraus ein Scene Layer generiert.

  16. LOD3 Gebäude - Hamburg

    • hub.arcgis.com
    • opendataportal-esri-konferenz-esri-training.opendata.arcgis.com
    • +1more
    Updated Jun 27, 2023
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    Esri Deutschland (2023). LOD3 Gebäude - Hamburg [Dataset]. https://hub.arcgis.com/maps/6bf427edbdcb40aab8e227172c4d3123
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    Dataset updated
    Jun 27, 2023
    Dataset provided by
    ESRIhttp://esri.com/
    Authors
    Esri Deutschland
    Area covered
    Description

    Aus dem Transparenzportal Hamburg: "Das 3D-Gebäudemodell LoD3.0-HH basiert auf einer manuellen Auswertung der Dachlandschaft. Alle Gebäude und Bauwerke auf Hamburger Stadtgebiet (ausgenommen die Inseln Neuwerk und Scharhörn) wurden photogrammetrisch ausgewertet und dreidimensional modelliert. Dabei wurden nicht nur die Gebäude aus dem amtlichen Kataster genutzt, sondern alle Gebäude, die zum Zeitpunkt der Datenerfassung in Hamburg existierten. Datengrundlage für die Auswertung ist ein Nadir- und Schrägbildflug aus dem Jahr 2020. Dabei wurde eine detaillierte Dachlandschaft modelliert, die über die übliche LoD2-Klassifikation hinausgeht. Signifikante Dachüberstände wurden in der Regel ebenso erfasst wie Dachaufbauten über einer Größe von einem Quadratmeter. Aus den Schrägluftbildern wurden abschließend Texturen für alle Gebäude mit einer datenschutz-konformen Auflösung von 20cm generiert.Die Gebäudemodelle sind auf einem Digitalen Geländemodell platziert, das mit einer Auflösung von fünf Metern unter der Berücksichtigung von Bruchkanten berechnet wurde.Der aktuell verfügbare Datensatz beinhaltet den ersten Ausschnitt des neuen Stadtmodells und beinhaltet ca. 34.000 Gebäudemodelle vornehmlich aus dem Innenstadtbereich (Area 1).Grundlage sind Luftbilder und ALKIS-Daten aus März 2020"

  17. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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(2024). Input and output data for the automated development of building floor plans for map services and city models | gimi9.com [Dataset]. https://gimi9.com/dataset/eu_620975229629235200

Input and output data for the automated development of building floor plans for map services and city models | gimi9.com

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Dataset updated
Jul 8, 2024
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

The offer includes data from the mFUND project "LevelOut - Automated development of building floor plans for map services and city models": Input data from digital building models in IFC format and output data for map services and city models, such as CityGML, IndoorGMl and OSM. In the project, digital building models of publicly accessible buildings were prepared in such a way that their interior data can be automatically extracted and integrated into data sets of the urban outdoor space. The generated data can thus serve as a basis for navigation applications for people and autonomous objects. On this basis, innovative applications can improve accessibility in public spaces, increase the attractiveness of rail and public transport, make transport and work processes safer and more efficient, and enable autonomous navigation with AI. -- This offer contains data from the mFUND project LevelOut: input data of digital building models in IFC format and output data for map services and city models, such as CityGML, IndoorGMl and OSM. In the project, digital building models of publicly accessible buildings were processed in such a way that their indoor data can be automatically extracted and be integrated into data sets of the urban outdoor space. The generated data can then be used as a basis for navigation applications for people and autonomous objects. On this basis, innovative applications can contribute to improved accessibility in public spaces, increase attractiveness of rail and public transport, make transportation and work processes safer and more efficient and enable autonomous navigation with AI.

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