Deep learning models benchmarked for offshore wind infrastructure monitoring

Researchers have benchmarked various deep learning models for classifying events related to offshore wind infrastructure using Sentinel-1 satellite data. The study compared ten different model training variants, including LSTM, Transformer, and fully connected architectures with varying context awareness and self-supervised pretraining. A supervised BiLSTM model demonstrated the best performance, significantly improving the AUC score and perfect match rate compared to a rule-based baseline. Further analysis using an ensemble of BiLSTM predictions and baseline labels allowed for the isolation of turbine deployment phases, revealing regional differences in deployment durations across China, the EU, and the UK. AI

IMPACT This research could improve the efficiency and accuracy of monitoring critical infrastructure, potentially informing policy and investment in renewable energy.

RANK_REASON The cluster contains an academic paper detailing a benchmark comparison of deep learning models for a specific application. [lever_c_demoted from research: ic=1 ai=1.0]

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• BiLSTM
• China
• EU
• European Space Agency
• long short-term memory
• Sentinel-1
• Transformer++
• UK

• paper
• other

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