Raster dataset on the area, volume and population of structures in Denmark at ten by ten-meter resolution January 2021.

Published: 10 September 2021| Version 1 | DOI: 10.17632/mpkdmfdb8m.1
Contributor:
Casper Fibaek

Description

Structural characteristics in a 10 by 10 meter resolution over Denmark. Data collected in January 2021. Contains three rasters over Denmark. - Area of human-made structures - Volume of human-made structures - Number of inhabitants pixel pixel inside human-made structures The data was gathered publicly available data. The source data is: - GeoDenmark Building Footprints - DAGI Danish Administrative boundaries - Danish Population Statistics at parish level - Danish National Terrain models, surface and terrain - National Address API for address access points The data does not cover the Island of Bornholm.

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Steps to reproduce

Area Structure footprints from GeoDenmark were first rasterized to a 40 by 40cm resolution raster, then resampled using GDAL using the “average” method to 10 by 10m and aligned with Sentinel 2 imagery. The resulting raster was then multiplied by 100 so that the values correspond to the area of any given pixel covered by a structure in m2. Volume In their surface and terrain editions, the Danish national terrain models were subtracted to create a nationwide map of the height above terrain. The height of the terrain was then clipped to the footprints of the buildings and resampled as the area data. The result is the volume of structures covering any given building per pixel in m3. Population The population at the parish level were distributed among each access point address within the parish. Access points addresses with population information were then joined to the structures, and the population was distributed amongst every pixel covered by the building. The structures were resampled in the same fashion as the area dataset. The result is a raster of the number of people living within structures at any given pixel in Denmark. The map is a mix of daytime and nighttime population.

Categories

Machine Learning, Applied Computer Science, Earth Observation

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