To address the energy crisis and the challenge of global climate change, it has become a consensus among countries to vigorously develop renewable energy sources, and wind energy, as a clean, efficient, and nonpolluting renewable energy source, is being promoted and generated in an increasing number of offshore wind turbine (OWT) arrays worldwide. However, accurate and complete offshore wind turbine datasets are crucial for ensuring the safety of marine navigation, marine ecological, and environmental protection, and the effective evaluation and optimization of offshore wind farms. However, most previous studies focused on OWT information extraction using a single multispectral or SAR image dataset, failing to combine the respective advantages of multispectral and SAR imagery. Moreover, accurate land boundary data are needed to mask the land before extracting OWTs, which is inconvenient to perform. In view of the shortcomings of previous studies, the advantages of multispectral and radar satellite image data are fully exploited, and offshore China is selected as the study area. According to the spatial location characteristics of OWTs, a new extraction algorithm for OWTs, the double-loop cooperative detection algorithm, is designed. At the end of 2022, a total of 5986 OWTs were detected in Chinese waters, with an extraction precision of 99.93%, a recall rate of 99.38%, and a comprehensive evaluation index value of 99.65%. The advantages of this algorithm are that it is fast, concise, and effective, thus providing a new approach for extracting OWTs.

With the rapid development of the offshore wind power industry in recent years, its effects on local social, economic and ecological environments have attracted widespread attention. Therefore, a timely understanding of the development status of offshore wind power, specifically, offshore wind turbines (OWTs) is crucial for the healthy and sustainable development of the offshore wind power industry. However, existing OWT detection methods often struggle to achieve timely, high-precision end-to-end detection of OWTs. To address this, in this study, an OWT detection network (OWT-DNet) based on multimodal remote sensing data is proposed. This network integrates Sentinel-1 synthetic aperture radar (SAR) imagery and Sentinel-2 optical imagery, effectively addressing the insufficient semantic information inherent in single-modal data for OWT detection. Experiments across five global test regions demonstrate that OWT-DNet achieves detection accuracy, recall, and comprehensive evaluation metrics that exceed 99.9%. Furthermore, OWT-DNet demonstrates outstanding detection performance under complex weather conditions. Comparative and ablation experiments validate the network's superior capability in OWT detection tasks. Overall, timely, high-precision end-to-end OWT detection is achieved for the first time on the basis of multimodal remote sensing data. Furthermore, an inaugural multimodal OWT sample dataset is established, laying a solid foundation for future OWT detection research.

Island coastline segmentation plays a crucial role in remote sensing image processing, especially when the island environment is complex and the scale is small, making segmentation challenging. The complex morphology of the islands and the small islands are the main causes of boundary blurring and topological discontinuity in the segmentation of the island coast. Therefore, this study proposes GTSegNet, an island coastline segmentation method designed to address the issues of boundary blurring and topological discontinuity in complex backgrounds. First, by introducing the Graph Contextual Modeling Module (GCB), the model captures global information and addresses the issue of neglected local features due to complex backgrounds and scale differences, thereby improving the model’s ability to discern blurry boundaries. Secondly, the Morphological Topology-Aware Refinement Module (TARM) is used for boundary sharpening and false response suppression, specifically addressing the issue of topological discontinuity, thus improving the accuracy of boundary localization and the continuity of topological structures. The two modules work synergistically, significantly improving the accuracy of the boundaries and topological continuity of the island coastline. Training and comparative experiments conducted on the newly constructed island coastline dataset demonstrate that GTSegNet achieves an outstanding performance with an mIoU of 96.96% and a Recall of 98.54%. Compared to other remote sensing semantic segmentation methods, GTSegNet consistently exhibits stable advantages in both quantitative and qualitative assessments, showcasing its great potential for large-scale marine mapping and macro-scale monitoring tasks.

Accurate mapping of offshore aquaculture remains challenging in complex coastal environments due to heterogeneous backgrounds, variable sea states, blurred pond boundaries, adhesion among densely distributed cages, and the weak texture of floating rafts. To address these limitations, this study proposes a bi-temporal multimodal cross-fusion network (BMCF-Net) for fine-scale offshore aquaculture segmentation from Sentinel-1/2 imagery. The framework jointly exploits bi-temporal observations acquired during non-ice and sea-ice periods and integrates them through a bi-temporal fusion module to enhance target–background separability and suppress environmental noise. In addition, a feature correction module and a multi-head feature fusion module are introduced to strengthen cross-modal alignment between SAR structural information and optical spectral–textural cues, thereby improving the separation of dense aquaculture units and the detection of weak-texture targets. Experiments conducted on a multimodal dataset from the Liaoning coastal zone show that BMCF-Net achieves F1-scores of 93.15%, 93.90%, and 89.04% for aquaculture ponds, cages, and floating rafts, respectively, outperforming state-of-the-art segmentation models such as FTransUNet. The proposed model was further applied to produce a high-resolution aquaculture distribution map for Liaoning Province in 2023 and to analyze area dynamics over the past six years. The results demonstrate the potential of BMCF-Net for large-scale offshore aquaculture monitoring and coastal resource management.