Observation Instance

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{
    "ob_id": 44921,
    "uuid": "b687c4d9dd374aafb3034efe005c0ee5",
    "title": "GLAMOUR: Global building morphology dataset for urban climate modelling (2020)",
    "abstract": "The GLobAl building MOrphology dataset for URban hydroclimate modelling (GLAMOUR) is derived from open-source Sentinel imagery that captures the average building height and footprint at a resolution of 0.0009° across urbanized areas worldwide (approximately 100 m at the equator) across 13189 urban areas globally from 01/01/2020 to 31/12/2020. This dataset optimally leverages multi-task DL (MTDL) models, publicly accessible satellite images in conjunction with the Google Cloud ecosystem to enable efficient and accurate large-scale mapping. This building morphology dataset provides an unprecedented possibility for enabling various urban hydroclimate applications at a global scale, including human thermal comfort simulation, building energy modelling, 3D flood risk analysis among others. \r\n\r\nData are netCDF formatted and contain the following variables:\r\n  - BH: building height (m)\r\n  - BF: building footprint (m2 m-2)\r\n\r\nEach file is named following the convention: `GLAMOUR_{lon_start}_{lon_end}_{lat_start}_{lat_end}.nc`, where:\r\n  - {lon_start} and {lon_end} are the longitude coordinates of the lower-left and upper-right corners of the grid, respectively.\r\n  - {lat_start} and {lat_end} are the latitude coordinates of the lower-left and upper-right corners of the grid, respectively.",
    "keywords": "GLAMOUR,Building morphology,Hydroclimate modelling",
    "publicationState": "published",
    "dataPublishedTime": "2025-11-06T14:35:20",
    "doiPublishedTime": null,
    "updateFrequency": "notPlanned",
    "status": "pending",
    "result_field": {
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        "dataPath": "/badc/deposited2025/GLAMOUR",
        "oldDataPath": [],
        "storageLocation": "internal",
        "storageStatus": "online",
        "volume": 1255955369,
        "numberOfFiles": 261,
        "fileFormat": "NetCDF"
    },
    "timePeriod": "https://api.catalogue.ceda.ac.uk/api/v2/times/12736/?format=api",
    "geographicExtent": "https://api.catalogue.ceda.ac.uk/api/v2/bboxes/4891/?format=api",
    "nonGeographicFlag": false,
    "phenomena": [
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        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/57405/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/57406/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/63367/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/92546/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/92547/?format=api"
    ],
    "dataLineage": "GLAMOUR is produced using a multi-task deep learning model SHAFTS based on Sentinel-1 and Sentinel-2 satellite images. The SHAFTS model is trained on a large-scale dataset of building footprints and heights from OpenStreetMap and Google Earth Engine. The SHAFTS model is then used to predict building footprints and heights for urban areas identified based population density outlined by the Gridded Population of the World, Version 4 (GPWv4) dataset and the Global Human Settlement-Urban Centre Database (GHS-UCDB).",
    "removedDataTime": null,
    "removedDataReason": "",
    "language": "English",
    "identifier_set": [],
    "projects": [
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        "https://api.catalogue.ceda.ac.uk/api/v2/rpis/215242/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/rpis/215243/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/rpis/215238/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/rpis/215245/?format=api",
        "https://api.catalogue.ceda.ac.uk/api/v2/rpis/215244/?format=api"
    ],
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            "accessCategory": "public",
            "accessRoles": null,
            "label": "public: None group",
            "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/",
            "licenceClassifications": "any"
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    "discoveryKeywords": []
}