The　traditional　landslide　identification　is　mainly　through　remote　sensing　visual　interpretation　and　human-computer　interaction　identification,　which　has　the　problems　of　time-consuming　and　laborious,　subjective　and　low　extraction　accuracy.　In　this　paper,　a　highway　in　Fujian　Province　is　taken　as　the　experimental　area,　and　a　rapid　landslide　identification　method　based　on　high-resolution　remote　sensing　data　for　mountainous　roads　in　southeast　Fujian　is　proposed.　Firstly,　by　analyzing　and　studying　landslide　features　such　as　hue,　topography,　spectrum,　vegetation　index　and　texture　using　sub-meter　high-resolution　remote　sensing　images,　the　multi-dimensional　multi-scale　landslide　classification　and　identification　rules　applicable　to　the　mountainous　roads　in　southeast　Fujian　are　established.　Secondly,　a　road　landslide　identification　model　is　constructed　by　combining　the　classification　tool　of　machine　learning　algorithm　with　the　multi-dimensional　multi-scale　feature　screening　set,　and　the　preliminary　identification　of　landslides　based　on　high-resolution　remote　sensing　images　in　terms　of　hue.　Finally,　the　landslides　are　further　segmented　and　extracted　by　slope,　normalized　vegetation　index　and　texture　feature　screening　set,　so　as　to　accurately　identify　the　spatial　distribution　of　landslides　on　mountainous　roads.　The　average　accuracy　of　landslide　identification　by　this　method　reaches　85.73%,　and　the　morphological　features　extracted　from　the　landslides　are　complete,　which　can　clearly　show　the　＂tongue＂　and　＂dustpan＂　shape　of　the　landslides,　and　also　clearly　identify　the　slide　walls　and　piles　of　the　landslides.　The　research　results　can　provide　scientific　reference　for　landslide　identification　and　risk　assessment　of　mountainous　traffic　routes　in　vegetation　development　areas　in　China.　©　2023　Science　Press.　All　rights　reserved.