发表机构
German Aerospace Center (DLR); University of Wuerzburg（德国航空航天中心; 维尔茨堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究对比10种深度学习模型，发现监督式BiLSTM在Sentinel-1海上风电时间序列分类中表现最优，结合集成方法提升性能后，分析得出中、欧盟、英的风机中位部署时长。
AI 中文摘要
海上风电基础设施生命周期（尤其是部署阶段）的监测，是利益相关方在部署活动日益频繁的阶段做出明智决策的重要支撑。欧空局（ESA）的Sentinel-1合成孔径雷达（SAR）任务生成了庞大的数据档案，可实现对海上风电基础设施的全球监测。将这些海量档案转化为信息，需要算法从全球尺度的密集时间序列中自动提取单一事件标签。本研究对10种深度学习模型训练变体进行结构化对比，用于基于Sentinel-1的海上风电基础设施时间序列密集分类，旨在推进该任务的基于规则的事件分类。我们训练了LSTM、Transformer和全连接模型变体，分别具备单时间、单向和双向上下文感知能力，每种变体均包含有无自监督预训练的情况。其中，监督式BiLSTM表现最佳，将目标AUC分数从基于规则的基线的0.7853提升至0.8509，完美匹配率从0.3508提升至0.5063。将BiLSTM的预测结果与现有基线标签结合，采用最小化标签转换的集成方法，进一步提升了与测试数据的一致性。利用这些改进后的标签，我们在全球尺度上分离出单个风机的部署阶段，并开展了2016年1月1日至2025年3月31日的区域和次区域分析，得出的中位部署时长分别为：中国84天、欧盟242天、英国258天。在跨多个空间尺度的分析结果中，部署相关驱动因素（包括补贴等法律法规及环境条件）清晰显现。
英文摘要
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.
Comments27 pages, 14 figures
