To address the problems of fragmentation， misclassification， and omission that often occur in high-resolution UAV remote sensing-based island waterline segmentation， this study proposes MF-MaxViT（Moga-Semantic FPN-MaxViT）， a dedicated model for instantaneous island waterline segmentation from UAV remote sensing imagery. The model employs MaxViT as the backbone and integrates both the Moga module and Semantic FPN， with key improvements as follows： Multi-axis attention mechanism： incorporating block attention and grid attention within MaxViT to enable the joint extraction of fine-grained local details and global semantic features； Moga（Multi-order gated aggregation） module integration： embedding the Moga module within MaxViT blocks to decompose features， extract multi-order contextual information， and perform feature aggregation， thereby enhancing the representation of multi-scale context； Semantic feature pyramid： employing a Semantic FPN（Semantic feature pyramid network） structure for multi-scale feature fusion， which strengthens the collaborative representation of deep semantic and shallow boundary information.Experimental results demonstrate that MF-MaxViT achieves an mIoU（Mean intersection over union，mIoU） of 94.40% on a self-constructed UAV remote sensing dataset， representing an improvement of 0.95% over the original MaxViT. The model also attains an mPA（Mean pixel accuracy，mPA） of 97.15% and an OA（Overall accuracy，OA） of 97.14%， outperforming mainstream models including DeepLab v3+， CrossFormer， U-Net， and TransUNet. In addition， MF-MaxViT also demonstrates strong cross-dataset generalization capability on two public satellite remote sensing datasets， the China Coastal Region dataset and the YTU-WaterNet dataset.In summary， MF-MaxViT provides a high-precision， robust approach for UAV-based instantaneous island waterline segmentation， offering reliable technical support for dynamic waterline monitoring and ecological risk assessment applications.