Image　segmentation　of　sea-land　remote　sensing　images　is　of　great　importance　for　downstream　applications　including　shoreline　extraction,　the　monitoring　of　near-shore　marine　environment,　and　near-shore　target　recognition.　To　mitigate　large　number　of　parameters　and　improve　the　segmentation　accuracy,　we　propose　a　new　Squeeze-Depth-Wise　UNet　(SDW-UNet)　deep　learning　model　for　sea-land　remote　sensing　image　segmentation.　The　proposed　SDW-UNet　model　leverages　the　squeeze-excitation　and　depth-wise　separable　convolution　to　construct　new　convolution　modules,　which　enhance　the　model　capacity　in　combining　multiple　channels　and　reduces　the　model　parameters.　We　further　explore　the　effect　of　position-encoded　information　in　NLP　(Natural　Language　Processing)　domain　on　sea-land　segmentation　task.　We　have　conducted　extensive　experiments　to　compare　the　proposed　network　with　the　mainstream　segmentation　network　in　terms　of　accuracy,　the　number　of　parameters　and　the　time　cost　for　prediction.　The　test　results　on　remote　sensing　data　sets　of　Guam,　Okinawa,　Taiwan,　San　Diego,　and　Diego　Garcia　demonstrate　the　effectiveness　of　SDW-UNet　in　recognizing　different　types　of　sea-land　areas　with　a　smaller　number　of　parameters,　reduces　prediction　time　cost　and　improves　performance　over　other　mainstream　segmentation　models.　We　also　show　that　the　position　encoding　can　further　improve　the　accuracy　of　model　segmentation.　©　2023　CRL　Publishing.　All　rights　reserved.