14 datasets found
  1. European Cities: Cartosat-1 Euro-Maps 3D

    • earth.esa.int
    Updated Sep 28, 2022
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    European Space Agency (2022). European Cities: Cartosat-1 Euro-Maps 3D [Dataset]. https://earth.esa.int/eogateway/catalog/european-cities-cartosat-1-euro-maps-3d
    Explore at:
    Dataset updated
    Sep 28, 2022
    Dataset authored and provided by
    European Space Agencyhttp://www.esa.int/
    License

    http://inspire.ec.europa.eu/metadata-codelist/LimitationsOnPublicAccess/INSPIRE_Directive_Article13_1ahttp://inspire.ec.europa.eu/metadata-codelist/LimitationsOnPublicAccess/INSPIRE_Directive_Article13_1a

    Area covered
    Europe
    Description

    A large number of European cities are covered by this dataset; for each city you can find one or more Cartosat-1 ortho image products and one or more Euro-Maps 3D DSM tiles clipped to the extent of the ortho coverage. The Euro-Maps 3D DSM is a homogeneous, 5 m spaced Digital Surface Model semi-automatically derived from 2.5 m Cartosat-1 in-flight stereo data with a vertical accuracy of 10 m. The very detailed and accurate representation of the surface is achieved by using a sophisticated and well adapted algorithm implemented on the basis of the Semi-Global Matching approach. The final product includes several pixel-based quality and traceability layers: The dsm layer (_dsm.tif) contains the elevation heights as a geocoded raster file The source layer (_src.tif) contains information about the data source for each height value/pixel The number layer (_num.tif) contains for each height value/pixel the number of IRS-P5 Cartosat-1 stereo pairs used for the generation of the DEM The quality layer (_qc.tif) is set to 1 for each height/pixel value derived from IRS-P5 Cartosat-1 data and which meets or exceeds the product specifications The accuracy vertical layer (*_acv.tif) contains the absolute vertical accuracy for each quality controlled height value/pixel. The ortho image is a Panchromatic image at 2.5 m resolution. The following table defines the offered product types. EO-SIP product type Description PAN_PAM_3O IRS-P5 Cartosat-1 ortho image DSM_DEM_3D IRS-P5 Cartosat-1 DSM

  2. s

    Cities, Europe, Year 1100

    • searchworks.stanford.edu
    zip
    Updated Apr 9, 2025
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    (2025). Cities, Europe, Year 1100 [Dataset]. https://searchworks.stanford.edu/view/mr065vx2502
    Explore at:
    zipAvailable download formats
    Dataset updated
    Apr 9, 2025
    Area covered
    Europe
    Description

    This layer is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.

  3. s

    Cities, Europe, Year 1600

    • searchworks.stanford.edu
    zip
    Updated Jun 8, 2024
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    (2024). Cities, Europe, Year 1600 [Dataset]. https://searchworks.stanford.edu/view/kz527ft5373
    Explore at:
    zipAvailable download formats
    Dataset updated
    Jun 8, 2024
    Area covered
    Europe
    Description

    This layer is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.

  4. f

    Mapping Europe into local climate zones

    • plos.figshare.com
    pdf
    Updated May 31, 2023
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    Matthias Demuzere; Benjamin Bechtel; Ariane Middel; Gerald Mills (2023). Mapping Europe into local climate zones [Dataset]. http://doi.org/10.1371/journal.pone.0214474
    Explore at:
    pdfAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Matthias Demuzere; Benjamin Bechtel; Ariane Middel; Gerald Mills
    License

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

    Area covered
    Europe
    Description

    Cities are major drivers of environmental change at all scales and are especially at risk from the ensuing effects, which include poor air quality, flooding and heat waves. Typically, these issues are studied on a city-by-city basis owing to the spatial complexity of built landscapes, local topography and emission patterns. However, to ensure knowledge sharing and to integrate local-scale processes with regional and global scale modelling initiatives, there is a pressing need for a world-wide database on cities that is suited for environmental studies. In this paper we present a European database that has a particular focus on characterising urbanised landscapes. It has been derived using tools and techniques developed as part of the World Urban Database and Access Portal Tools (WUDAPT) project, which has the goal of acquiring and disseminating climate-relevant information on cities worldwide. The European map is the first major step toward creating a global database on cities that can be integrated with existing topographic and natural land-cover databases to support modelling initiatives.

  5. s

    Cities, Europe, Year 1300

    • searchworks.stanford.edu
    zip
    Updated Jul 21, 2013
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    (2013). Cities, Europe, Year 1300 [Dataset]. https://searchworks.stanford.edu/view/rc225zx5942
    Explore at:
    zipAvailable download formats
    Dataset updated
    Jul 21, 2013
    Area covered
    Europe
    Description

    This layer is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.

  6. Z

    Data from: 3DHD CityScenes: High-Definition Maps in High-Density Point...

    • data.niaid.nih.gov
    Updated Jul 16, 2024
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    Fingscheidt, Tim (2024). 3DHD CityScenes: High-Definition Maps in High-Density Point Clouds [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_7085089
    Explore at:
    Dataset updated
    Jul 16, 2024
    Dataset provided by
    Fingscheidt, Tim
    Plachetka, Christopher
    Sertolli, Benjamin
    Klingner, Marvin
    Fricke, Jenny
    License

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

    Description

    Overview

    3DHD CityScenes is the most comprehensive, large-scale high-definition (HD) map dataset to date, annotated in the three spatial dimensions of globally referenced, high-density LiDAR point clouds collected in urban domains. Our HD map covers 127 km of road sections of the inner city of Hamburg, Germany including 467 km of individual lanes. In total, our map comprises 266,762 individual items.

    Our corresponding paper (published at ITSC 2022) is available here. Further, we have applied 3DHD CityScenes to map deviation detection here.

    Moreover, we release code to facilitate the application of our dataset and the reproducibility of our research. Specifically, our 3DHD_DevKit comprises:

    Python tools to read, generate, and visualize the dataset,

    3DHDNet deep learning pipeline (training, inference, evaluation) for map deviation detection and 3D object detection.

    The DevKit is available here:

    https://github.com/volkswagen/3DHD_devkit.

    The dataset and DevKit have been created by Christopher Plachetka as project lead during his PhD period at Volkswagen Group, Germany.

    When using our dataset, you are welcome to cite:

    @INPROCEEDINGS{9921866, author={Plachetka, Christopher and Sertolli, Benjamin and Fricke, Jenny and Klingner, Marvin and Fingscheidt, Tim}, booktitle={2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)}, title={3DHD CityScenes: High-Definition Maps in High-Density Point Clouds}, year={2022}, pages={627-634}}

    Acknowledgements

    We thank the following interns for their exceptional contributions to our work.

    Benjamin Sertolli: Major contributions to our DevKit during his master thesis

    Niels Maier: Measurement campaign for data collection and data preparation

    The European large-scale project Hi-Drive (www.Hi-Drive.eu) supports the publication of 3DHD CityScenes and encourages the general publication of information and databases facilitating the development of automated driving technologies.

    The Dataset

    After downloading, the 3DHD_CityScenes folder provides five subdirectories, which are explained briefly in the following.

    1. Dataset

    This directory contains the training, validation, and test set definition (train.json, val.json, test.json) used in our publications. Respective files contain samples that define a geolocation and the orientation of the ego vehicle in global coordinates on the map.

    During dataset generation (done by our DevKit), samples are used to take crops from the larger point cloud. Also, map elements in reach of a sample are collected. Both modalities can then be used, e.g., as input to a neural network such as our 3DHDNet.

    To read any JSON-encoded data provided by 3DHD CityScenes in Python, you can use the following code snipped as an example.

    import json

    json_path = r"E:\3DHD_CityScenes\Dataset\train.json" with open(json_path) as jf: data = json.load(jf) print(data)

    1. HD_Map

    Map items are stored as lists of items in JSON format. In particular, we provide:

    traffic signs,

    traffic lights,

    pole-like objects,

    construction site locations,

    construction site obstacles (point-like such as cones, and line-like such as fences),

    line-shaped markings (solid, dashed, etc.),

    polygon-shaped markings (arrows, stop lines, symbols, etc.),

    lanes (ordinary and temporary),

    relations between elements (only for construction sites, e.g., sign to lane association).

    1. HD_Map_MetaData

    Our high-density point cloud used as basis for annotating the HD map is split in 648 tiles. This directory contains the geolocation for each tile as polygon on the map. You can view the respective tile definition using QGIS. Alternatively, we also provide respective polygons as lists of UTM coordinates in JSON.

    Files with the ending .dbf, .prj, .qpj, .shp, and .shx belong to the tile definition as “shape file” (commonly used in geodesy) that can be viewed using QGIS. The JSON file contains the same information provided in a different format used in our Python API.

    1. HD_PointCloud_Tiles

    The high-density point cloud tiles are provided in global UTM32N coordinates and are encoded in a proprietary binary format. The first 4 bytes (integer) encode the number of points contained in that file. Subsequently, all point cloud values are provided as arrays. First all x-values, then all y-values, and so on. Specifically, the arrays are encoded as follows.

    x-coordinates: 4 byte integer

    y-coordinates: 4 byte integer

    z-coordinates: 4 byte integer

    intensity of reflected beams: 2 byte unsigned integer

    ground classification flag: 1 byte unsigned integer

    After reading, respective values have to be unnormalized. As an example, you can use the following code snipped to read the point cloud data. For visualization, you can use the pptk package, for instance.

    import numpy as np import pptk

    file_path = r"E:\3DHD_CityScenes\HD_PointCloud_Tiles\HH_001.bin" pc_dict = {} key_list = ['x', 'y', 'z', 'intensity', 'is_ground'] type_list = ['

  7. o

    Driven distance by commuters to the center of major and minor cities in...

    • explore.openaire.eu
    • zenodo.org
    Updated Apr 28, 2023
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    Noémie Jeannin (2023). Driven distance by commuters to the center of major and minor cities in Europe [Dataset]. http://doi.org/10.5281/zenodo.7876125
    Explore at:
    Dataset updated
    Apr 28, 2023
    Authors
    Noémie Jeannin
    Area covered
    Europe
    Description

    The two files contains respectively a map of driving distances around major and minor cities in Europe. The distance is given by range of 5 km from 5 to 45 km in a resolution of 100m*100m. The encoding is uint8. The metropolitan areas considered in this study are those from this study of functional urban area definition. The corresponding geofile is available at this adress. Most of the cities with more than 50'000 habitants are included, some cities have been removed or added depending on the local context. As the geofile contains only polygones of the metropolitan areas and not the city point, the points form cities with the number of habitants have been dowloaded from Natural Data and then selected by comparison with the polygones. Cities with more than 20'000 have also been selected as commuting center for rural areas. Only the cities outside the metropolitan commuting zones are included. The calculation of driving distances around centers have been performed with OpenRouteService (personal API key required, free of charge).

  8. s

    GISCO - Urban Audit 2004, Nov. 2009

    • geodcat-ap.semic.eu
    • sdi.eea.europa.eu
    Updated Sep 24, 2020
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    (2020). GISCO - Urban Audit 2004, Nov. 2009 [Dataset]. https://geodcat-ap.semic.eu/csw-4-web/eea-csw/resource/bb5ab4fd-add7-4849-b89f-22d42a198932
    Explore at:
    https://geodcat-ap.semic.eu/csw-4-web/eea-csw/resource/bb5ab4fd-add7-4849-b89f-22d42a198932#_sid=rd24Available download formats
    Dataset updated
    Sep 24, 2020
    Variables measured
    https://geodcat-ap.semic.eu/csw-4-web/eea-csw/resource/bb5ab4fd-add7-4849-b89f-22d42a198932
    Description

    When using this data set, it should be bibliographically referred to as 'Urban Audit, 2004'. The Urban Audit (UA) provides European urban statistics for a representative sample of large and medium-sized cities across 30 European countries. It enables an assessment of the state of individual EU cities and provides access to comparative information from other EU cities. This spatial dataset will support the study and dissemination of the UA data. It allows the visualisation of participating cities at three conceptual levels: - UA City - the core city, using an administrative definition - UA City Kernel - a concept introduced to improve comparability between large cities - Larger Urban Zone (LUZ) - approximating the functional urban region In addition, this spatial dataset allows visualisation of a 285 participating cities at two hierarchical sublevels to analyse the disparities within cities: - Sub City Districts level 1 (SCD L1) - Sub City Districts level 2 (SCD L2) The extent of this dataset is the EU 27 (2007) plus Croatia (HR), Norway (NO) and Switzerland (CH). The URAU_2004 dataset contains a polygonal feature class for UA Cities, UA City Kernels and Large Urban Zones, derived from the geometry of the GISCO COMM_2004 dataset (based on EuroBoundary Map 2004). Polygonal feature classes for Sub City Districts are derived from the geometry of the GISCO COMM_2004 dataset (based on EuroBoundary Map 2004) or spatial data supplied by URAU delegates which has been made coincident with UA City geometry. A generalised version of each feature class allows for visualisation at the scale of 1:3 Million. UA Cities are also represented by a point topology that are derived from and synchronised with the GISCO STTL_V3 dataset of European Settlements. The UA city points are, when possible, synchronised to an Urban Fabric class in Corine Land Cover 2000.

  9. Data from: 3DHD CityScenes: High-Definition Maps in High-Density Point...

    • zenodo.org
    bin, pdf
    Updated Jul 16, 2024
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    Christopher Plachetka; Benjamin Sertolli; Jenny Fricke; Marvin Klingner; Tim Fingscheidt; Christopher Plachetka; Benjamin Sertolli; Jenny Fricke; Marvin Klingner; Tim Fingscheidt (2024). 3DHD CityScenes: High-Definition Maps in High-Density Point Clouds [Dataset]. http://doi.org/10.5281/zenodo.7085090
    Explore at:
    bin, pdfAvailable download formats
    Dataset updated
    Jul 16, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Christopher Plachetka; Benjamin Sertolli; Jenny Fricke; Marvin Klingner; Tim Fingscheidt; Christopher Plachetka; Benjamin Sertolli; Jenny Fricke; Marvin Klingner; Tim Fingscheidt
    License

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

    Description

    Overview

    3DHD CityScenes is the most comprehensive, large-scale high-definition (HD) map dataset to date, annotated in the three spatial dimensions of globally referenced, high-density LiDAR point clouds collected in urban domains. Our HD map covers 127 km of road sections of the inner city of Hamburg, Germany including 467 km of individual lanes. In total, our map comprises 266,762 individual items.

    Our corresponding paper (published at ITSC 2022) is available here.
    Further, we have applied 3DHD CityScenes to map deviation detection here.

    Moreover, we release code to facilitate the application of our dataset and the reproducibility of our research. Specifically, our 3DHD_DevKit comprises:

    • Python tools to read, generate, and visualize the dataset,
    • 3DHDNet deep learning pipeline (training, inference, evaluation) for
      map deviation detection and 3D object detection.

    The DevKit is available here:

    https://github.com/volkswagen/3DHD_devkit.

    The dataset and DevKit have been created by Christopher Plachetka as project lead during his PhD period at Volkswagen Group, Germany.

    When using our dataset, you are welcome to cite:

    @INPROCEEDINGS{9921866,
      author={Plachetka, Christopher and Sertolli, Benjamin and Fricke, Jenny and Klingner, Marvin and 
      Fingscheidt, Tim},
      booktitle={2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)}, 
      title={3DHD CityScenes: High-Definition Maps in High-Density Point Clouds}, 
      year={2022},
      pages={627-634}}

    Acknowledgements

    We thank the following interns for their exceptional contributions to our work.

    • Benjamin Sertolli: Major contributions to our DevKit during his master thesis
    • Niels Maier: Measurement campaign for data collection and data preparation

    The European large-scale project Hi-Drive (www.Hi-Drive.eu) supports the publication of 3DHD CityScenes and encourages the general publication of information and databases facilitating the development of automated driving technologies.

    The Dataset

    After downloading, the 3DHD_CityScenes folder provides five subdirectories, which are explained briefly in the following.

    1. Dataset

    This directory contains the training, validation, and test set definition (train.json, val.json, test.json) used in our publications. Respective files contain samples that define a geolocation and the orientation of the ego vehicle in global coordinates on the map.

    During dataset generation (done by our DevKit), samples are used to take crops from the larger point cloud. Also, map elements in reach of a sample are collected. Both modalities can then be used, e.g., as input to a neural network such as our 3DHDNet.

    To read any JSON-encoded data provided by 3DHD CityScenes in Python, you can use the following code snipped as an example.

    import json
    
    json_path = r"E:\3DHD_CityScenes\Dataset\train.json"
    with open(json_path) as jf:
      data = json.load(jf)
    print(data)

    2. HD_Map

    Map items are stored as lists of items in JSON format. In particular, we provide:

    • traffic signs,
    • traffic lights,
    • pole-like objects,
    • construction site locations,
    • construction site obstacles (point-like such as cones, and line-like such as fences),
    • line-shaped markings (solid, dashed, etc.),
    • polygon-shaped markings (arrows, stop lines, symbols, etc.),
    • lanes (ordinary and temporary),
    • relations between elements (only for construction sites, e.g., sign to lane association).

    3. HD_Map_MetaData

    Our high-density point cloud used as basis for annotating the HD map is split in 648 tiles. This directory contains the geolocation for each tile as polygon on the map. You can view the respective tile definition using QGIS. Alternatively, we also provide respective polygons as lists of UTM coordinates in JSON.

    Files with the ending .dbf, .prj, .qpj, .shp, and .shx belong to the tile definition as “shape file” (commonly used in geodesy) that can be viewed using QGIS. The JSON file contains the same information provided in a different format used in our Python API.

    4. HD_PointCloud_Tiles

    The high-density point cloud tiles are provided in global UTM32N coordinates and are encoded in a proprietary binary format. The first 4 bytes (integer) encode the number of points contained in that file. Subsequently, all point cloud values are provided as arrays. First all x-values, then all y-values, and so on. Specifically, the arrays are encoded as follows.

    • x-coordinates: 4 byte integer
    • y-coordinates: 4 byte integer
    • z-coordinates: 4 byte integer
    • intensity of reflected beams: 2 byte unsigned integer
    • ground classification flag: 1 byte unsigned integer

    After reading, respective values have to be unnormalized. As an example, you can use the following code snipped to read the point cloud data. For visualization, you can use the pptk package, for instance.

    import numpy as np
    import pptk
    
    file_path = r"E:\3DHD_CityScenes\HD_PointCloud_Tiles\HH_001.bin"
    pc_dict = {}
    key_list = ['x', 'y', 'z', 'intensity', 'is_ground']
    type_list = ['

    5. Trajectories

    We provide 15 real-world trajectories recorded during a measurement campaign covering the whole HD map. Trajectory samples are provided approx. with 30 Hz and are encoded in JSON.

    These trajectories were used to provide the samples in train.json, val.json. and test.json with realistic geolocations and orientations of the ego vehicle.

    • OP1 – OP5 cover the majority of the map with 5 trajectories.
    • RH1 – RH10 cover the majority of the map with 10 trajectories.

    Note that OP5 is split into three separate parts, a-c. RH9 is split into two parts, a-b. Moreover, OP4 mostly equals OP1 (thus, we speak of 14 trajectories in our paper). For completeness, however, we provide all recorded trajectories here.

  10. a

    Larger Urban Zone, Urban Audit 2004

    • hub.arcgis.com
    Updated Feb 21, 2017
    + more versions
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    Centre d'enseignement Saint-Joseph de Chimay (2017). Larger Urban Zone, Urban Audit 2004 [Dataset]. https://hub.arcgis.com/datasets/a61d33e6197440cc96b4daba54d6b567
    Explore at:
    Dataset updated
    Feb 21, 2017
    Dataset authored and provided by
    Centre d'enseignement Saint-Joseph de Chimay
    Area covered
    Description

    When using this data set, it should be bibliographically referred to as 'Urban Audit, 2004'.

    The Urban Audit (UA) provides European urban statistics for a representative sample of large and medium-sized cities across 30 European countries. It enables an assessment of the state of individual EU cities and provides access to comparative information from other EU cities.

    This spatial dataset will support the study and dissemination of the UA data. It allows the visualisation of participating cities at three conceptual levels: - UA City - the core city, using an administrative definition - UA City Kernel - a concept introduced to improve comparability between large cities - Larger Urban Zone (LUZ) - approximating the functional urban region

    In addition, this spatial dataset allows visualisation of a 285 participating cities at two hierarchical sublevels to analyse the disparities within cities: - Sub City Districts level 1 (SCD L1) - Sub City Districts level 2 (SCD L2)

    The extent of this dataset is the EU-27 plus Croatia (HR), Norway (NO) and Switzerland (CH).

    The URAU_2004 dataset contains a polygonal feature class for UA Cities, UA City Kernels and Large Urban Zones, derived from the geometry of the GISCO COMM_2004 dataset (based on EuroBoundary Map 2004). Polygonal feature classes for Sub City Districts are derived from the geometry of the GISCO COMM_2004 dataset (based on EuroBoundary Map 2004) or spatial data supplied by URAU delegates which has been made coincident with UA City geometry.

    A generalised version of each feature class allows for visualisation at the scale of 1:3 Million. UA Cities are also represented by a point topology that are derived from and synchronised with the GISCO STTL_V3 dataset of European Settlements. The UA city points are, when possible, synchronised to an Urban Fabric class in Corine Land Cover 2000.

  11. Driven distance by commuters to the center of major and minor cities in...

    • zenodo.org
    tiff
    Updated Jul 12, 2024
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    Noémie Jeannin; Noémie Jeannin (2024). Driven distance by commuters to the center of major and minor cities in Europe [Dataset]. http://doi.org/10.5281/zenodo.7876126
    Explore at:
    tiffAvailable download formats
    Dataset updated
    Jul 12, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Noémie Jeannin; Noémie Jeannin
    License

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

    Area covered
    Europe
    Description

    The two files contains respectively a map of driving distances around major and minor cities in Europe. The distance is given by range of 5 km from 5 to 45 km in a resolution of 100m*100m. The encoding is uint8.

    The metropolitan areas considered in this study are those from this study of functional urban area definition.

    The corresponding geofile is available at this adress.

    Most of the cities with more than 50'000 habitants are included, some cities have been removed or added depending on the local context.

    As the geofile contains only polygones of the metropolitan areas and not the city point, the points form cities with the number of habitants have been dowloaded from Natural Data and then selected by comparison with the polygones.

    Cities with more than 20'000 have also been selected as commuting center for rural areas. Only the cities outside the metropolitan commuting zones are included.

    The calculation of driving distances around centers have been performed with OpenRouteService (personal API key required, free of charge).

  12. s

    Cities, Europe, Year 300

    • searchworks.stanford.edu
    zip
    Updated May 30, 2024
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    (2024). Cities, Europe, Year 300 [Dataset]. https://searchworks.stanford.edu/view/bq686xc5803
    Explore at:
    zipAvailable download formats
    Dataset updated
    May 30, 2024
    Area covered
    Europe
    Description

    This layer is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.

  13. d

    Damage and protection cost curves for coastal flooding at the 600 largest...

    • search.dataone.org
    • doi.pangaea.de
    Updated Feb 14, 2018
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    Prahl, Boris F; Boettle, Markus; Costa, Luis; Rybski, Diego; Kropp, Jürgen P (2018). Damage and protection cost curves for coastal flooding at the 600 largest coastal cities within Europe [Dataset]. http://doi.org/10.1594/PANGAEA.875254
    Explore at:
    Dataset updated
    Feb 14, 2018
    Dataset provided by
    PANGAEA Data Publisher for Earth and Environmental Science
    Authors
    Prahl, Boris F; Boettle, Markus; Costa, Luis; Rybski, Diego; Kropp, Jürgen P
    Area covered
    Description

    Costs of coastal flooding and protection are essential information for risk assessment and natural hazards research, but there are few systematic attempts to quantify cost curves beyond the case study level. Here, we present a set of systematically derived damage and protection cost curves for the 600 largest (by area) European coastal cities. The city clusters were identified by an automated cluster algorithm from CORINE land cover 2012 data, following the Urban Morphological Zone (UMZ) definition. The data provides detailed cost curves for direct flood damages at flood heights between 0 and 12 m on a 0.5 m increment. Costs estimates are based on depth damage functions for different land use obtained from the European Joint Research Center. The necessary mapping between land use and land cover is based on Land Use/Cover Area frame Survey (LUCAS) 2015 primary data. The underlying inundation maps were derived from the European Digital Elevation Model (EU-DEM). Furthermore, the data contain curves for the cost of protection at the same heights and increments as the damage curves, assuming no previously installed protection. These curves are available both for a low and high cost scenario and are based on hypothetical protection courses derived from cluster data and inundation maps. All cost estimates are given in Euro and were inflation-adjusted to 2016 price levels. For spatial reference, we include the individual raster tiles depicting the extent of each city cluster. The research leading to these results has received funding from the European Community's Seventh Framework Programme under Grant Agreement No. 308497 (Project RAMSES).

  14. s

    Cities, Europe, Year 700

    • searchworks.stanford.edu
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    Updated May 8, 2022
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    (2022). Cities, Europe, Year 700 [Dataset]. https://searchworks.stanford.edu/view/rk490hz9977
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    zipAvailable download formats
    Dataset updated
    May 8, 2022
    Area covered
    Europe
    Description

    This layer is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.

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European Space Agency (2022). European Cities: Cartosat-1 Euro-Maps 3D [Dataset]. https://earth.esa.int/eogateway/catalog/european-cities-cartosat-1-euro-maps-3d
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European Cities: Cartosat-1 Euro-Maps 3D

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Dataset updated
Sep 28, 2022
Dataset authored and provided by
European Space Agencyhttp://www.esa.int/
License

http://inspire.ec.europa.eu/metadata-codelist/LimitationsOnPublicAccess/INSPIRE_Directive_Article13_1ahttp://inspire.ec.europa.eu/metadata-codelist/LimitationsOnPublicAccess/INSPIRE_Directive_Article13_1a

Area covered
Europe
Description

A large number of European cities are covered by this dataset; for each city you can find one or more Cartosat-1 ortho image products and one or more Euro-Maps 3D DSM tiles clipped to the extent of the ortho coverage. The Euro-Maps 3D DSM is a homogeneous, 5 m spaced Digital Surface Model semi-automatically derived from 2.5 m Cartosat-1 in-flight stereo data with a vertical accuracy of 10 m. The very detailed and accurate representation of the surface is achieved by using a sophisticated and well adapted algorithm implemented on the basis of the Semi-Global Matching approach. The final product includes several pixel-based quality and traceability layers: The dsm layer (_dsm.tif) contains the elevation heights as a geocoded raster file The source layer (_src.tif) contains information about the data source for each height value/pixel The number layer (_num.tif) contains for each height value/pixel the number of IRS-P5 Cartosat-1 stereo pairs used for the generation of the DEM The quality layer (_qc.tif) is set to 1 for each height/pixel value derived from IRS-P5 Cartosat-1 data and which meets or exceeds the product specifications The accuracy vertical layer (*_acv.tif) contains the absolute vertical accuracy for each quality controlled height value/pixel. The ortho image is a Panchromatic image at 2.5 m resolution. The following table defines the offered product types. EO-SIP product type Description PAN_PAM_3O IRS-P5 Cartosat-1 ortho image DSM_DEM_3D IRS-P5 Cartosat-1 DSM

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