Quickly　extracting　road　networks　from　high-resolution　remote　sensing　images　is　crucial　in　mapping,　urban　planning,　and　GIS　databases　updating.　Semi-automatic　road　extraction,　as　the　main　method　of　road　surveying　and　mapping,　is　a　labor-intensive　task.　In　order　to　reduce　the　cost　of　manual　intervention　and　improve　extraction　efficiency,　this　paper　proposes　a　fast　road　centerline　extraction　algorithm　based　on　geodesic　distance　field.　First,　the　optimal　circular　template　is　proposed　to　automatically　estimated　the　road　width　and　adjust　the　manual　seeds　to　road　center　based　on　the　morphological　gradient　map,　and　the　road　saliency　map　is　calculated　according　to　the　local　color　features　inside　the　templates.　Second,　we　propose　the　soft　road　center　kernel　density　based　on　road　saliency　map　which　overcomes　the　difficulty　of　threshold　presetting　of　road　segmentation　in　traditional　road　center　kernel　density　estimation.　Most　importantly,　a　geodesic　distance　field　is　proposed　to　quickly　extract　the　geodesic　curve　between　two　consecutive　seeds,　which　dramatically　increase　the　efficiency　of　our　algorithm.　Finally,　we　introduce　the　mean　filter　into　our　scheme　to　smooth　the　road　centerlines.　Extensive　experiments　and　quantitative　comparisons　show　that　the　proposed　algorithm　can　greatly　reduce　manual　intervention　without　losing　much　accuracy,　and　significantly　improve　the　efficiency　of　road　extraction.　Furthermore,　the　proposed　algorithm　takes　almost　the　same　time　to　extract　any　length　of　road　centerline　given　fixed　image　size,　and　no　hyperparameters　need　to　be　set.　The　algorithm　behaves　good　experience　in　human-computer　interaction.　©　2023　SinoMaps　Press.　All　rights　reserved.