Accurate　road　network　information　is　required　to　study　and　analyze　the　relationship　between　land　usage　type　and　land　subsidence,　and　road　extraction　from　remote　sensing　images　is　an　important　data　source　for　updating　road　networks.　This　task　has　been　considered　a　significant　semantic　segmentation　problem,　given　the　many　road　extraction　methods　developed　for　remote　sensing　images　in　recent　years.　Although　impressive　results　have　been　achieved　by　classifying　each　pixel　in　the　remote　sensing　image　using　a　semantic　segmentation　network,　traditional　semantic　segmentation　methods　often　lack　clear　constraints　of　road　features.　Consequently,　the　geometric　features　of　the　results　might　deviate　from　actual　roads,　leading　to　issues　like　road　fractures,　rough　edges,　inconsistent　road　widths,　and　more,　which　hinder　their　effectiveness　in　road　updates.　This　paper　proposes　a　novel　road　semantic　segmentation　algorithm　for　remote　sensing　images　based　on　the　joint　road　angle　prediction.　By　incorporating　the　angle　prediction　module　and　the　angle　feature　fusion　module,　constraints　are　added　to　the　angle　features　of　the　road.　Through　the　angle　prediction　and　angle　feature　fusion,　the　information　contained　in　the　remote　sensing　images　can　be　better　utilized.　The　experimental　results　show　that　the　proposed　method　outperforms　existing　semantic　segmentation　methods　in　both　quantitative　evaluation　and　visual　effects.　Furthermore,　the　extracted　roads　were　consecutive　with　distinct　edges,　making　them　more　suitable　for　mapping　road　updates.