The structural information of offshore oil production equipment is the basis for the functional modification and upgrading of offshore oil drilling platforms. In order to solve the problems of low efficiency in the process of acquiring offshore oil production equipment structure information by traditional measurement methods, we propose a deep learning-based point cloud data processing scheme for offshore oil production equipment. First, a point cloud dataset of offshore oil production equipment for deep learning is constructed, and a deep learning network based on the two-step downsampling method and the local feature aggregation of each point after downsampling is implemented for the semantic segmentation of the dataset. Second, the combined point cloud filtering process based on radius filtering and statistical filtering is implemented on the segmented point cloud data. Third, an implicit surface reconstruction based on contextual prior information is implemented for offshore oil production equipment components. The dataset contains six types of point clouds, including pipelines, flanges, shelves, bends, valves, and oil recovery trees. Based on this dataset for semantic segmentation, the overall segmentation accuracy reaches 98.87% and the mIoU reaches 92.79%. Combined filtering is performed on the segmented offshore oil production equipment data, and the denoising rate can reach 94%. Finally, the denoised point cloud data is utilized for 3-D reconstruction, and the overall consistency accuracy can reach 1.85 mm, which can provide fast, efficient, and reliable data support for the upgrading of offshore oil rigs.

Tidal creeks are vital geomorphological features of tidal flats, and their spatial and temporal variations contribute significantly to the preservation of ecological diversity and the spatial evolution of coastal wetlands. Traditional methods, such as manual annotation and machine learning, remain common for tidal creek extraction, but they are slow and inefficient. With increasing data volumes, accurately analyzing tidal creeks over large spatial and temporal scales has become a significant challenge. This study proposes a residual U-Net model that utilizes full-dimensional dynamic convolution to segment tidal creeks in the Yellow River Delta, employing Gaofen-2 satellite images with a resolution of 4 m. The model replaces the traditional convolutions in the residual blocks of the encoder with Omni-dimensional Dynamic Convolution (ODConv), mitigating the loss of fine details and improving segmentation for small targets. Adding coordinate attention (CA) to the Atrous Spatial Pyramid Pooling (ASPP) module improves target classification and localization in remote sensing images. Including dice coefficients in the focal loss function improves the model’s gradient and tackles class imbalance within the dataset. Furthermore, the inclusion of dice coefficients in the focal loss function improves the gradient of the model and tackles the dataset’s class inequality. The study results indicate that the model attains an F1 score and kappa coefficient exceeding 80% for both mud and salt marsh regions. Comparisons with several semantic segmentation models on the mud marsh tidal creek dataset show that ODU-Net significantly enhances tidal creek segmentation, resolves class imbalance issues, and delivers superior extraction accuracy and stability.

Altimetry is widely used in the research of oceanic phenomena. Particularly, wide-swath interferometric altimetry (WIA) can be used to measure sea surface height (SSH) with sufficiently high resolution. Although it has provided that the steric height can be used to validate the SSH by the theoretical and the observation data, validating sea-level variability in the two dimensions of WIA remains a difficult task. Site selection and influencing factor analysis are important in the validation process. In this paper, we analyze the validation process at three sites (Panyu site, DJxxxx site, and Wanshan site) in the South China Sea and discuss the effects of different sites on the validation of SSH variability. On this basis, we analyze the influencing factors, which include significant wave heights, wind, and currents, at three sites. Based on the results, we discuss how site selection for the validation process of WIA can be improved. At DJxxxx site, greater water depth and more equipment layout schemes have emerged with better performance in wavenumber. At the same time, the performance under the tidal, wave, wind and current is different for the other two sites. However, further analysis is necessary for the site selection.

Accurate as-built models of offshore platforms require component-level information from dense laser scans, but point-wise annotation is costly. We present Offshore Semi Supervised Contrastive Segmentation (OSCS), a framework for drilling platform point clouds when only a few complete scenes are annotated. OSCS constructs fixed-point-count 3D nearest-neighbor crops and uses a shared hierarchical attention encoder–decoder for supervised classification and contrastive representation learning. In unlabeled scenes, retained point identities establish positive correspondences between overlapping views. Predicted classes, confidence gating, and cross-batch class-wise feature queues organize contrastive samples without converting predictions into classification targets. Experiments used the Offshore Drilling Platform Tree Dataset (ODPT), with additional evaluation on Stanford Large-Scale 3D Indoor Spaces (S3DIS). At label budgets of approximately 10% and 20% on ODPT, OSCS achieved 88.03 ± 1.06% and 89.69 ± 0.97% mean intersection over union (mIoU) across three labeled-scene selections. The corresponding mean gains over matched supervised baselines were 4.57 and 4.42 percentage points. In the original fully labeled method comparison, OSCS achieved 92.27% mIoU, 2.67 percentage points above the strongest competing result under that protocol. Under the S3DIS Area 5 evaluation protocol, OSCS achieved 70.23% mIoU after retraining on the remaining areas. OSCS provides component-wise semantic point sets from partly annotated offshore scans, supporting the preparation of geometric information for retrofit planning and onshore prefabrication.

Recently, interferometric altimeters (IA), such as the Surface Water and Ocean Topography (SWOT) satellite, have been launched, and will improve the observation of ocean dynamics. The validation of altimeters is a complex and important process that can effectively improve their observation accuracy. For the IA validation of sea surface height in two-dimensional, it is normal to use the steric height to validate the sea surface height. However, the validation method is affected by many factors. In order to improve the validation accuracy, the South China Sea, which is a suitable area for validation of China’s offshore, was selected in this study to analyze the impact of wind on the validation procedures. Through the analysis of the spatial-temporal relationships between wind speed, steric height and sea surface height (SH-SSH), we found correlations between wind speed and validation results. The high wind speeds can lead to a certain deviation in the relationship between SH and SSH, and the special weather conditions make it unsuitable for validation. Meanwhile, in terms of time, May is the time when the wind speed is relatively low, making it more suitable for validation. These analyses are useful for the validation procedures in sea surface height.