Accurate observations of Ocean Surface Wind Speed (OSWS) are vital for predicting extreme weather and understanding ocean–atmosphere interactions. However, spaceborne sensors (e.g., ASCAT, SMAP) often experience data loss due to harsh weather and instrument malfunctions. Existing inpainting methods often rely on reanalysis data that is released with delays, which restricts their real-time capability. Additionally, deep-learning-based methods, such as Transformers, face challenges due to their high computational complexity. To address these challenges, we present the Multimodal Wind Speed Inpainting Dataset (MWSID), which integrates 12 auxiliary forecasting variables to support real-time OSWS inpainting. Based on MWSID, we propose the MMamba framework, combining the Multimodal Feature Extraction module, which uses mutual information (MI) theory to optimize feature selection, and the OSWS Reconstruction module, which employs Attention-Mamba-2 within a Residual-in-Residual-Dense architecture for efficient OSWS inpainting. Experiments show that MMamba outperforms MambaIR (state-of-the-art) with an RMSE of 0.5481 m/s and an SSIM of 0.9820, significantly reducing RMSE by 21.10% over Kriging and 8.22% over MambaIR in high-winds (>15 m/s). We further introduce MMamba-L, a lightweight 0.22M-parameter variant suitable for resource-limited devices. These contributions make MMamba and MWSID powerful tools for OSWS inpainting, benefiting extreme weather prediction and oceanographic research.

Point cloud semantic segmentation is a key technology for applications such as autonomous driving, robotics, and virtual reality. Current approaches are heavily reliant on local relative coordinates and simplistic attention mechanisms to aggregate neighborhood information. This often leads to an ineffective joint representation of geometric perturbations and feature variations, coupled with a lack of adaptive selection for salient features during context fusion. On this basis, we propose LSSCC-Net, a novel segmentation framework based on LACV-Net. First, the spatial-feature dynamic aggregation module is designed to fuse offset information by symmetric interaction between spatial positions and feature channels, thus supplementing local structural information. Second, a dual-dimensional attention mechanism (spatial and channel) is introduced to symmetrically deploy attention modules in both the encoder and decoder, prioritizing salient information extraction. Finally, Lovász-Softmax Loss is used as an auxiliary loss to optimize the training objective. The proposed method is evaluated on two public benchmark datasets. The mIoU on the Toronto3D and S3DIS datasets is 83.6% and 65.2%, respectively. Compared with the baseline LACV-Net, LSSCC-Net showed notable improvements in challenging categories: the IoU for “road mark” and “fence” on Toronto3D increased by 3.6% and 8.1%, respectively. These results indicate that LSSCC-Net more accurately characterizes complex boundaries and fine-grained structures, enhancing segmentation capabilities for small-scale targets and category boundaries.