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Global offshore wind turbine dataset

Version 10 2022-01-09, 06:04
Version 5 2021-04-10, 07:38
dataset
posted on 2021-04-10, 07:38 authored by Ting ZhangTing Zhang, Bo Tian, Dhritiraj Sengupta, Lei Zhang, Yali Si

This dataset including two contents.

One part is the global offshore wind turbine dataset constructed by us, which provide geocoded information on global offshore wind turbines (OWTs) derived from Sentinel-1 synthetic aperture radar (SAR) time-series images from 2015 to 2019. It identified 6,924 wind turbines comprising of more than 10 nations. Data is available at 10 m spatial resolution, providing an explicit dataset for planning, monitoring, and managing marine space. The global OWTs are stored in Shapefile (.shp) format. The attributes and metadata are organized with referenced to the WGS84 datum, and each record consists of seven attributes: centroid latitude (centr_lat), centroid longitude (centr_lon), continent, country, sea area (sea_area), appearance year (occ_year) and month (occ_month).

Another part is the validation set we generated that consisted of 50 random offshore wind farms, covering 2,663 wind turbines using three methods. Reference data include (1) the high-resolution aerial imagery and Google images, (2) the comparison and corroboration across multiple source datasets, and (3) a comprehensive visual examination and an extensive internal review by the authors using Sentinel 2-MSI data or Landsat 8-OLI imagery under true colour composition and Sentinel 1 data after floating or temporarily mobile object removal. The validation set is stored in the Shapefile (.shp) format for each wind farm. The attributes and metadata are organized with referenced to the WGS84 datum, and each record consists of three attributes: centroid latitude (centr_lat), centroid longitude (centr_lon) and country. Besides, one of the reference data, Sentinel 1 data after floating or temporarily mobile object removal, were attached.

Funding

the National Key Research and Development Program of China (No.2018YFE0101000), the Ministry of Science and Technology of the People's Republic of China (No. 2016YFE0133700)

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