Road　segmentation　methods　based　on　deep　neural　networks　have　achieved　great　success　in　recent　years,　but　creating　accurate　pixel-wise　training　labels　is　still　a　boring　and　expensive　task,　especially　for　large-scale　high-resolution　remote　sensing　images　(HRSIs).　Inspired　by　the　stacked　hourglass　model　for　human　joints　detection,　we　propose　a　weakly　supervised　road　segmentation　method　using　point　annotations　in　this　article.　First,　we　design　a　patch-based　deep　convolutional　neural　network　(DCNN)　model　for　road　seeds　and　background　points　detection　and　train　the　model　using　point　annotations.　Then,　in　the　process　of　road　segmentation,　the　DCNN　model　detects　a　series　of　road　and　background　points　that　are　used　to　train　a　Support　Vector　Machine　Classifier　(SVC)　for　classifying　each　pixel　into　road　or　nonroad.　According　to　the　local　geometry　of　road　and　the　inaccurate　classification　of　SVC,　a　multiscale　and　multidirection　Gabor　filter　(MMGF)　is　put　forward　to　estimate　the　road　potential.　Finally,　the　active　contour　model　based　on　local　binary　fitting　energy　(LBF-Snake)　is　introduced　to　extract　the　road　regions　from　the　inhomogeneous　road　potential.　Qualitative　and　quantitative　comparisons　show　that　our　method　achieves　results　close　to　the　fully　supervised　semantic　methods　without　considering　the　annotation　cost　and　outperforms　them　given　a　fixed　budget.