Ultra-high　resolution　image　segmentation　has　raised　increasing　interests　in　recent　years　due　to　its　realistic　applications.　In　this　paper,　we　innovate　the　widely　used　high-resolution　image　segmentation　pipeline,　in　which　an　ultra-high　resolution　image　is　partitioned　into　regular　patches　for　local　segmentation　and　then　the　local　results　are　merged　into　a　high-resolution　semantic　mask.　In　particular,　we　introduce　a　novel　locality-aware　context　fusion　based　segmentation　model　to　process　local　patches,　where　the　relevance　between　local　patch　and　its　various　contexts　are　jointly　and　complementarily　utilized　to　handle　the　semantic　regions　with　large　variations.　Additionally,　we　present　the　alternating　local　enhancement　module　that　restricts　the　negative　impact　of　redundant　information　introduced　from　the　contexts,　and　thus　is　endowed　with　the　ability　of　fixing　the　locality-aware　features　to　produce　refined　results.　Furthermore,　in　comprehensive　experiments,　we　demonstrate　that　our　model　outperforms　other　state-of-the-art　methods　in　public　benchmarks　and　verify　the　effectiveness　of　the　proposed　modules.　Our　released　codes　will　be　available　at:　https://github.com/liqiokkk/FCtL.