Semantic　segmentation　of　remote　sensing　images　(RSIs)　is　of　great　significance　for　obtaining　geospatial　object　information.　Transformers　win　promising　effect,　whereas　multi-head　self-attention　(MSA)　is　expensive.　We　propose　an　efficient　semantic　segmentation　Transformer　(ESST)　of　RSIs　that　combines　zero-padding　position　encoding　with　linear　space　reduction　attention　(LSRA).　First,　to　capture　the　coarse-to-fine　features　of　RSI,　a　zero-padding　position　encoding　is　proposed　by　adding　overlapping　patch　embedding　(OPE)　layers　and　convolution　feed-forward　networks　(CFFN)　to　improve　the　local　continuity　of　features.　Then,　we　replace　LSRA　in　the　attention　operation　to　extract　multi-level　features　to　reduce　the　computational　cost　of　the　encoder.　Finally,　we　design　a　lightweight　all　multi-layer　perceptron　(all-MLP)　head　decoder　to　easily　aggregate　multi-level　features　to　generate　multi-scale　features　for　semantic　segmentation.　Experimental　results　demonstrate　that　our　method　produces　a　trade-off　in　accuracy　and　speed　for　semantic　segmentation　of　RSIs　on　the　Potsdam　and　Vaihingen　datasets,　respectively.