LAUSR

Offshore wind farm projects are being promoted in the seas surrounding the Korean Peninsula to secure renewable energy. To support site selection, offshore wind resource maps were generated using deep neural networks trained on Sentinel-1 SAR imagery, numerical weather prediction data, offshore wind observations, sea surface temperature, and bathymetry. The deep neural network (DNN) framework consisted of six sub-models targeting eastward and northward wind components across three regions—the Yellow Sea, Korea Strait, and East Sea—to account for spatial heterogeneity. The proposed models outperformed existing approaches, achieving mean absolute errors (MAE) ranging from 1.31 to 1.69 m/s and correlation coefficients (CC) between 0.827 and 0.913. These DNN models were then applied to produce offshore wind energy maps at a 150 m resolution, effectively capturing seasonal and regional variability. The resulting high-resolution maps provide valuable insights for evaluating the suitability of existing wind farm sites and identifying potential new candidates.