The offshore wind energy sector is expanding rapidly, increasing the need for independent, high-temporal-resolution monitoring of infrastructure deployment and operation at global scale. While Earth Observation based offshore wind infrastructure mapping has matured for spatial localization, existing open datasets lack temporally dense and semantically fine-grained information on construction and operational dynamics. We introduce a global Sentinel-1 synthetic aperture radar (SAR) time series data corpus that resolves deployment and operational phases of offshore wind infrastructure from 2016Q1 to 2025Q1. Building on an updated object detection workflow, we compile 15,606 time series at detected infrastructure locations, with overall 14,840,637 events as analysis-ready 1D SAR backscatter profiles, one profile per Sentinel-1 acquisition and location. To enable direct use and benchmarking, we release (i) the analysis ready 1D SAR profiles, (ii) event-level baseline semantic labels generated by a rule-based classifier, and (iii) an expert-annotated benchmark dataset of 553 time series with 328,657 event labels. The baseline classifier achieves a macro F1 score of 0.84 in event-wise evaluation and an area under the collapsed edit similarity-quality threshold curve (AUC) of 0.785, indicating temporal coherence. We demonstrate that the resulting corpus supports global-scale analyses of deployment dynamics, the identification of differences in regional deployment patterns, vessel interactions, and operational events, and provides a reference for developing and comparing time series classification methods for offshore wind infrastructure monitoring.

翻译：海上风电行业正快速扩张，这增加了对基础设施部署与运营进行全球范围、高时间分辨率独立监测的需求。尽管基于地球观测的海上风电基础设施空间定位制图技术已趋成熟，但现有开放数据集缺乏关于建设与运营动态的密集时间序列和语义粒度信息。我们发布了一套全球Sentinel-1合成孔径雷达（SAR）时间序列数据语料库，可解析2016年第一季度至2025年第一季度期间海上风电基础设施的部署与运营阶段。基于更新的目标检测流程，我们在检测到的基础设施位置处编译了15,606条时间序列，总共包含14,840,637个事件，形成可直接用于分析的SAR一维背向散射剖面（每个Sentinel-1采集事件和位置对应一个剖面）。为便于直接使用和基准测试，我们公开了：(i) 可直接用于分析的一维SAR剖面，(ii) 通过基于规则的分类器生成的事件级基准语义标签，以及(iii) 包含553条时间序列、328,657个事件标签的专家标注基准数据集。该基准分类器在事件级评估中达到宏观F1分数0.84，在折叠编辑相似度-质量阈值曲线下面积（AUC）为0.785，表明时间一致性。我们证明，该语料库支持对部署动态的全球尺度分析、区域部署模式差异识别、船只交互及运营事件分析，并为开发与比较海上风电基础设施监测的时间序列分类方法提供了参考基准。