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Merge pull request #330 from FAIRiCUBE/stac-dist-shadow-index-city-of
ChangeType.update stac_dist/shadow_index_city_of_luxembourg/shadow_index_city_of_luxembourg.json
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stac_dist/shadow_index_city_of_luxembourg/shadow_index_city_of_luxembourg.json
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{ | ||
"type": "Feature", | ||
"stac_version": "1.0.0", | ||
"id": "shadow_index_city_of_luxembourg", | ||
"properties": { | ||
"keywords": [ | ||
"lidar", | ||
" terrain", | ||
" luref", | ||
" topography", | ||
" DEM", | ||
"shadow index", | ||
" luxembourg" | ||
], | ||
"license": "CC0-1.0", | ||
"description": "The shadow index quantifies the extent of shaded areas. ", | ||
"providers": [ | ||
{ | ||
"organization_name": "space4environment", | ||
"organization": "space4environment", | ||
"comments": "", | ||
"doc_link": "", | ||
"organization_email": "[email protected]", | ||
"ORCID_ID": "", | ||
"roles": "processor", | ||
"project_purpose": "Urban adaptation to climate change" | ||
}, | ||
{ | ||
"organization_name": "Administration du cadastre et de la topographie", | ||
"organization": "Administration du cadastre et de la topographie", | ||
"doc_link": "https://data.public.lu/fr/datasets/lidar-2019-modele-numerique-de-terrain-mnt/", | ||
"roles": "producer" | ||
} | ||
], | ||
"dataSource": "https://data.public.lu/fr/datasets/lidar-2019-modele-numerique-de-terrain-mnt/", | ||
"cube:dimensions": { | ||
"x": { | ||
"axis": "x", | ||
"extent": [ | ||
72850, | ||
82570 | ||
], | ||
"reference_system": "2169", | ||
"type": "spatial", | ||
"unit": "meter", | ||
"interpolation": "", | ||
"step": 10 | ||
}, | ||
"y": { | ||
"axis": "y", | ||
"extent": [ | ||
69590, | ||
80030 | ||
], | ||
"reference_system": "2169", | ||
"type": "spatial", | ||
"unit": "meter", | ||
"interpolation": "", | ||
"step": 10 | ||
}, | ||
"z": { | ||
"extent": [ | ||
null, | ||
null | ||
], | ||
"reference_system": "", | ||
"unit": "", | ||
"interpolation": "", | ||
"step": "", | ||
"type": "spatial" | ||
}, | ||
"time": { | ||
"type": "temporal", | ||
"extent": [ | ||
"2019-01-01T00:00Z", | ||
"2019-12-31T00:00Z" | ||
], | ||
"step": "P1Y0M0DT0H0M0S", | ||
"unit": "y", | ||
"interpolation": "" | ||
} | ||
}, | ||
"raster:bands": [ | ||
{ | ||
"band_name": "shadow index", | ||
"unit": "continuous", | ||
"data_type": "float32", | ||
"nodata": -99999, | ||
"definition": "The shadow index (SI) is the ratio of observed radiance to its normalization value and corresponds to the damping factor of radiation", | ||
"description": "The shadow index (SI) is the ratio of observed radiance to its normalization value and corresponds to the damping factor of radiation", | ||
"category_list": "", | ||
"comment": "", | ||
"interpolation": "" | ||
} | ||
], | ||
"title": "Shadow Index of the City of Luxembourg", | ||
"datasource_type": "grid", | ||
"area_cover": "5171.66 ha", | ||
"documentation": "https://data.public.lu/fr/datasets/lidar-2019-modele-numerique-de-terrain-mnt/", | ||
"crs": "2169", | ||
"start_datetime": "2019-01-01T00:00Z", | ||
"end_datetime": "2019-12-31T00:00Z", | ||
"re_projection_crs": "", | ||
"unit_of_measure": "", | ||
"resolution": "", | ||
"personalData": "no personal data", | ||
"provenance_name": "LiDAR 2019 - Digital Terrain Model (DTM)", | ||
"preprocessing": "A standard hillshade model was calculated using the Module Analytical Hillshading from SAGA-GIS (v2.2.0). This was used to calculate the shadow index. First a day of shadow was simulated for solar altitude for luxembourg city and secondly the data range was normalized linearly", | ||
"source_data": "https://data.public.lu/fr/datasets/lidar-2019-modele-numerique-de-terrain-mnt/", | ||
"models": "", | ||
"data_quality": "", | ||
"quality_control": "", | ||
"metada_standards": "", | ||
"distributions": "", | ||
"access_control": "fairicube account required ", | ||
"datetime": "2024-09-03T00:00Z", | ||
"modification": null, | ||
"provision": null, | ||
"use_case_S4E": "1", | ||
"ingestion_status": "", | ||
"platform": "Eox" | ||
}, | ||
"geometry": { | ||
"type": "Polygon", | ||
"coordinates": [ | ||
[ | ||
[ | ||
6.069104443926092, | ||
49.56094657618465 | ||
], | ||
[ | ||
6.069104443926092, | ||
49.65484833240523 | ||
], | ||
[ | ||
6.203715071874704, | ||
49.65484833240523 | ||
], | ||
[ | ||
6.203715071874704, | ||
49.56094657618465 | ||
], | ||
[ | ||
6.069104443926092, | ||
49.56094657618465 | ||
] | ||
] | ||
] | ||
}, | ||
"links": [ | ||
{ | ||
"rel": "root", | ||
"href": "../catalog.json", | ||
"type": "application/json", | ||
"title": "data-access catalog" | ||
}, | ||
{ | ||
"rel": "parent", | ||
"href": "../catalog.json", | ||
"type": "application/json", | ||
"title": "data-access catalog" | ||
}, | ||
{ | ||
"example:container": false, | ||
"example:language": "Python", | ||
"type": "text/x-python", | ||
"title": "AWS S3 Bucket API", | ||
"description": "The script shows how to open a gridded dataset stored on an S3 Bucket as an xarray DataSet", | ||
"href": "https://github.com/FAIRiCUBE/common-code/blob/main/access_data_apis/access_s3_bucket.py", | ||
"rel": "example" | ||
} | ||
], | ||
"assets": { | ||
"shadow_2019_10m_b1": { | ||
"href": "https://hub-fairicube0.s3.eu-central-1.amazonaws.com/data/d012_luxembourg/shadow_2019_10m_b1.tif", | ||
"roles": [ | ||
"data" | ||
] | ||
}, | ||
"shadow_2019_10m_b1_COG_overview": { | ||
"href": "https://hub-fairicube0.s3.eu-central-1.amazonaws.com/data/web/luxembourg/shadow_2019_10m_b1_COG.tif", | ||
"roles": [ | ||
"data" | ||
] | ||
}, | ||
"shadow_2019_10m_b1_thumbnail": { | ||
"href": "https://github.com/FAIRiCUBE/uc1-urban-climate/blob/master/images/thumbnails/shadow_2019_10m_b1.png", | ||
"roles": [ | ||
"thumbnail" | ||
] | ||
} | ||
}, | ||
"bbox": [ | ||
6.069104443926092, | ||
49.56094657618465, | ||
6.203715071874704, | ||
49.65484833240523 | ||
], | ||
"stac_extensions": [ | ||
"https://stac-extensions.github.io/raster/v1.1.0/schema.json", | ||
"https://stac-extensions.github.io/datacube/v2.0.0/schema.json" | ||
] | ||
} |