Abstract: In optical remote sensing images with complex scenes and rich land cover information，the sea-land segmentation faces challenges such as low positioning accuracy and blurred edges. Therefore，this paper proposed a deep convolutional network model and a sea-land segmentation method that integrate contextual semantic information and edge features. First，the rich target semantic information was extracted from remote sensing images using the FusionNet semantic segmentation network module. Then，multi-scale and hierarchical contextual semantic features were extracted from the segmentation network using the enhanced atrous spatial pyramid pooling （ASPP） module and contextual attention module. Additionally，an edge extraction sub-network was built to extract multi-scale edge features. Finally，the semantic features and edge features were combined through a fusion module，thereby achieving accurate sea-land segmentation. This method was tested with two typical representative datasets. The results showed that this method achieved an overall prediction accuracy of 98.21%，an F1 score of 97.64%，and a boundary F1 score of 89.36%，all significantly outperforming other models. Particularly in complex backgrounds，this method can effectively improve the accuracy of segmentation and edge detection，demonstrating definite advantages in the segmentation of artificial coastlines and ports.