Sea-land segmentation is an important basis for the application of remote sensing images, such as coastline change analysis and resource management. Due to the complex scene of remote sensing images and uneven distribution of land size and shape, sealand segmentation is faced with problems such as misclassification and unclear boundary segmentation. Aiming at the above problems, proposes a gated pyramid fusion network for sealand segmentation in remote sensing images. Firstly, two deep features are aggregated through the attentioninduced crosslayer aggregation module to capture the global context and accurately and roughly obtain the size and shape information of the land. Then, the aggregated global features are sent to the gated fusion module, guided by the global information, useful context information is selected from the multiscale features, optimizes boundary details layer by layer and highlights the entire land area. Finally, global supervision is performed on each side output. Two sets of remote sensing images from different data sources were selected for experiments, the accuracy was 99.13% and 98.98%, the F1 score was 99.03% and 98.89%, and the mIoU was 98.26% and 9797%, respectively. Experimental results show that this algorithm has better segmentation effect than other algorithms.