Semantic-to-Instance Segmentation of Time-Invariant Offshore Wind Farms Using Sentinel-1 Time Series and Time-Shift Augmentation

Osmar Luiz Ferreira de Carvalho, Osmar Abílio de Carvalho Junior (), Anesmar Olino de Albuquerque and Daniel Guerreiro e Silva
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Osmar Luiz Ferreira de Carvalho: Department of Electrical Engineering, University of Brasília, Brasília 70910-900, DF, Brazil
Osmar Abílio de Carvalho Junior: Department of Geography, University of Brasília, Brasília 70910-900, DF, Brazil
Anesmar Olino de Albuquerque: Department of Geography, University of Brasília, Brasília 70910-900, DF, Brazil
Daniel Guerreiro e Silva: Department of Electrical Engineering, University of Brasília, Brasília 70910-900, DF, Brazil

Energies, 2025, vol. 18, issue 5, 1-20

Abstract: The rapid expansion of offshore wind energy requires effective monitoring to balance renewable energy development with environmental and marine spatial planning. This study proposes a novel offshore wind farm detection methodology integrating Sentinel-1 SAR time series, a time-shift augmentation strategy, and semantic-to-instance segmentation transformation. The methodology consists of (1) constructing a dataset with offshore wind farms labeled from Sentinel-1 SAR time series, (2) applying a time-shift augmentation strategy by randomizing image sequences during training (avoiding overfitting due to chronological ordering), (3) evaluating six deep learning architectures (U-Net, U-Net++, LinkNet, DeepLabv3+, FPN, and SegFormer) across time-series lengths of 1, 5, 10, and 15 images, and (4) converting the semantic segmentation results into instance-level detections using Geographic Information System tools. The results show that increasing the time-series length from 1 to 15 images significantly improves performance, with the Intersection over Union increasing from 63.29% to 81.65% and the F-score from 77.52% to 89.90%, using the best model (LinkNet). Also, models trained with time-shift augmentation achieved a 25% higher IoU and an 18% higher F-score than those trained without it. The semantic-to-instance transformation achieved 99.7% overall quality in per-object evaluation, highlighting the effectiveness of our approach.

Keywords: deep learning; computer vision; remote sensing; renewable energy; wind energy; wind farms; radar (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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