Aiming　at　the　problem　that　the　background　of　high-resolution　remote　sensing　images　is　complex　and　road　extraction　is　easily　disturbed　by　background　information　such　as　shadows,　buildings,　and　railroads,　the　DAFCResUnet　model　with　lightweight　dual　attention　and　feature　compensation　mechanism　is　proposed　in　this　study.　The　model　is　based　on　ResUnet　and　achieves　a　balance　between　model　performance　and　spatiotemporal　complexity　by　adding　lightweight　dual　attention　and　feature　compensation　modules.　The　dual　attention　module　enhances　the　feature　extraction　capability　of　the　model,　and　the　feature　compensation　module　can　fuse　the　road　features　from　deep　and　shallow　layers　in　the　network.　The　experimental　results　using　DeepGlobe　and　GF-2　road　datasets　show　that　the　IoU　of　the　DAFCResUnet　model　can　reach　0.6713,　0.8033,　respectively,　and　the　F1-score　is　0.7402,　0.8507,　respectively.　The　overall　accuracy　of　the　model　is　higher　than　that　of　U-Net,　ResUnet,　and　VNet　models.　Compared　with　the　U-Net　and　ResUnet　models,　the　DAFCResUnet　model　only　increases　a　small　amount　of　computation　and　number　of　parameters,　but　the　IoU　and　F1-score　are　improved　substantially.　Compared　with　the　VNet　model,　the　DAFCResUnet　model　achieves　a　higher　accuracy　with　much　lower　computation　and　smaller　number　of　parameters,　and　the　model　has　advantages　in　both　accuracy　and　spatiotemporal　complexity.　Compared　with　the　other　models,　the　DAFCResUnet　model　has　stronger　feature　extraction　and　anti-interference　ability,　which　can　better　solve　the　commission　and　omission　caused　by　interfering　objects　on　the　road,　ground　features　similar　to　roads,　tree　shade　or　shadow　shading,　etc.　©　2022,　Science　Press.　All　right　reserved.