Junfu Wang

Mid-channel bars are fundamental fluvial geomorphic units that regulate sediment transport, channel stability, and riparian ecosystems, and their spatiotemporal evolution provides critical insights for sustainable river management. This study examines the structural reorganization and migration dynamics of mid-channel bars along the mainstem of the transboundary Yalu River using multi-temporal Sentinel-2 imagery acquired in 2019, 2022, and 2024. An automated extraction framework combining a dense atrous U-Net (DA-UNet) with multispectral indices was developed to robustly identify mid-channel bars under complex water–land transition conditions. Based on the extracted results, changes in bar number, area, size composition, morphological characteristics, and centroid migration were systematically analyzed. The results reveal a pronounced reorganization of mid-channel bars systems over the study period: although the number of bars increased from 111 to 136, the total area decreased from 168.97 km² to 165.00 km², indicating a transition from a “few-large” to a “many-small” configuration. Size-based analysis further shows an increase in small and medium bars, while large bars remained relatively stable, leading to a more differentiated multi-scale structure. These findings highlight the effectiveness of integrating multi-temporal remote sensing and deep learning for long-term monitoring of geomorphic dynamics and provide scientific evidence to support sustainable river regulation and transboundary watershed management.

Accurately extracting offshore floating raft aquaculture (FRA) areas from remotely sensed images is the key to rationally managing aquaculture resources. Currently, deep learning-based methods perform well in FRA area extraction tasks but are limited by the shortcomings of single-modality remote sensing data, which affect their extraction accuracies. To solve this problem, we constructed a multimodal dataset called CHN-YS3-FRA for FRA area extraction using heterogeneous Sentinel-1/-2 remote sensing image data and proposed a new multiscale synthetic aperture radar (SAR) guidance network (MSARG-Net) for performing FRA area extraction on multimodal remote sensing images. In this network, we designed a global-local Poolformer block (GLPB) to model the local and global relationships of FRA areas to more comprehensively learn the semantic features of these areas. In addition, we designed a multiscale SAR-guided attention (MSARGA) block to efficiently fuse the semantic information acquired from different modalities. The experimental results obtained on the CHN-YS3-FRA dataset show that MSARG-Net could robustly extract offshore FRA regions with F1 scores, intersection-over-union (IoU) and kappa coefficient values of 91.46%, 84.26% and 89.69%, respectively. Compared with the latest remote sensing-based semantic segmentation methods, MSARG-Net has achieved significant quantitative and qualitative improvements and has significant potential for mapping and monitoring large-scale offshore FRA areas. Our code and dataset are available at Pytorch-MSARG-Net.

Accurately extracting large-scale offshore floating raft aquaculture (FRA) areas is crucial for supporting scientific planning and precise aquaculture management. While remote sensing technology offers advantages such as wide coverage, rapid imaging, and multispectral capabilities for FRA monitoring, the current methods face challenges in terms of establishing spatial–spectral correlations and extracting multiscale features, thereby limiting their accuracy. To address these issues, we propose an innovative multiscale spatial–spectral fusion network (MSSFNet) designed specifically for extracting offshore FRA areas from multispectral remote sensing imagery. MSSFNet effectively integrates spectral and spatial information through a spatial–spectral feature extraction block (SSFEB), significantly enhancing the accuracy of FRA area identification. Additionally, a multiscale spatial attention block (MSAB) captures contextual information across different scales, improving the ability to detect FRA areas of varying sizes and shapes while minimizing edge artifacts. We created the CHN-YE7-FRA dataset using Sentinel-2 multispectral remote sensing imagery and conducted extensive evaluations. The results showed that MSSFNet achieved impressive metrics: an F1 score of 90.76%, an intersection over union (IoU) of 83.08%, and a kappa coefficient of 89.75%, surpassing those of state-of-the-art methods. The ablation results confirmed that the SSFEB and MSAB modules effectively enhanced the FRA extraction accuracy. Furthermore, the successful practical applications of MSSFNet validated its generalizability and robustness across diverse marine environments. These findings highlight the performance of MSSFNet in both experimental and real-world scenarios, providing reliable, precise FRA area monitoring. This capability provides crucial data for scientific planning and environmental protection purposes in coastal aquaculture zones.

The accurate extraction and monitoring of offshore aquaculture areas are crucial for the marine economy, environmental management, and sustainable development. Existing methods relying on unimodal remote sensing images are limited by natural conditions and sensor characteristics. To address this issue, we integrated multispectral imaging (MSI) and synthetic aperture radar imaging (SAR) to overcome the limitations of single-modal images. We propose a cross-modal multidimensional frequency perception network (CMFPNet) to enhance classification and extraction accuracy. CMFPNet includes a local–global perception block (LGPB) for combining local and global semantic information and a multidimensional adaptive frequency filtering attention block (MAFFAB) that dynamically filters frequency-domain information that is beneficial for aquaculture area recognition. We constructed six typical offshore aquaculture datasets and compared CMFPNet with other models. The quantitative results showed that CMFPNet outperformed the existing methods in terms of classifying and extracting floating raft aquaculture (FRA) and cage aquaculture (CA), achieving mean intersection over union (mIoU), mean F1 score (mF1), and mean Kappa coefficient (mKappa) values of 87.66%, 93.41%, and 92.59%, respectively. Moreover, CMFPNet has low model complexity and successfully achieves a good balance between performance and the number of required parameters. Qualitative results indicate significant reductions in missed detections, false detections, and adhesion phenomena. Overall, CMFPNet demonstrates great potential for accurately extracting large-scale offshore aquaculture areas, providing effective data support for marine planning and environmental protection. Our code is available at Data Availability Statement section.

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.