The　semantic　segmentation　of　remote　sensing　images　(RSIs)　is　important　in　a　variety　of　applications.　Conventional　encoder-decoder-based　convolutional　neural　networks　(CNNs)　use　cascade　pooling　operations　to　aggregate　the　semantic　information,　which　results　in　a　loss　of　localization　accuracy　and　in　the　preservation　of　spatial　details.　To　overcome　these　limitations,　we　introduce　the　use　of　the　high-resolution　network　(HRNet)　to　produce　high-resolution　features　without　the　decoding　stage.　Moreover,　we　enhance　the　low-to-high　features　extracted　from　different　branches　separately　to　strengthen　the　embedding　of　scale-related　contextual　information.　The　low-resolution　features　contain　more　semantic　information　and　have　a　small　spatial　size;　thus,　they　are　utilized　to　model　the　long-term　spatial　correlations.　The　high-resolution　branches　are　enhanced　by　introducing　an　adaptive　spatial　pooling　(ASP)　module　to　aggregate　more　local　contexts.　By　combining　these　context　aggregation　designs　across　different　levels,　the　resulting　architecture　is　capable　of　exploiting　spatial　context　at　both　global　and　local　levels.　The　experimental　results　obtained　on　two　RSI　datasets　show　that　our　approach　significantly　improves　the　accuracy　with　respect　to　the　commonly　used　CNNs　and　achieves　state-of-the-art　performance.