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This study benchmarks ten deep learning model variants for the dense classification of offshore wind infrastructure using Sentinel-1 Synthetic Aperture Radar (SAR) time series data. The research highlights the effectiveness of a supervised BiLSTM model, which significantly outperforms a rule-based baseline by raising the AUC score from 0.7853 to 0.8509 and improving the perfect match rate from 0.3508 to 0.5063. Additionally, the integration of BiLSTM predictions with baseline labels enhances agreement with test data, enabling a detailed analysis of deployment durations and influencing factors across various regions from 2016 to 2025.
A supervised BiLSTM model not only surpasses traditional rule-based methods but also reveals critical insights into offshore wind turbine deployment dynamics across global regions.
Monitoring of offshore wind energy infrastructure life cycles, especially during the deployment phase, is an important contribution for stakeholders to make informed decisions in a phase of increasing deployment activities. ESA's Sentinel-1 Synthetic Aperture Radar (SAR) mission produces large data archives that enable the global monitoring of offshore wind infrastructure. Turning these high-volume archives into information requires algorithms that automatically extract single event labels from dense time series at a global scale. In this study, we present a structured comparison of ten deep learning model-training variants for the dense classification of Sentinel-1 based offshore wind infrastructure time series, aiming to advance rule-based event classification of this task. We trained LSTM, Transformer, and fully connected model variants with monotemporal, unidirectional, and bidirectional context awareness, each with and without self-supervised pretraining. Among these, the supervised BiLSTM performs best, raising the target AUC score from 0.7853 for the rule-based baseline to 0.8509, and the perfect match rate from 0.3508 to 0.5063. Combining the BiLSTM predictions with the existing baseline labels in a label-transition-minimising ensemble further improves agreement with the test data. Using these improved labels, we isolate the deployment phase of individual turbines at a global scale and conduct a regional and subregional analysis covering 2016-01-01 to 2025-03-31, reporting median deployment durations of 84 d (China), 242 d (EU), and 258 d (UK). Deployment-related drivers, including legal regulations such as subsidies, and environmental conditions, emerge clearly from the analysed results across multiple spatial scales.