Individual　tree　extraction　from　urban　mobile　laser　scanning　(MLS)　point　clouds　is　important　for　many　urban　applications.　Recently,　deep　learning-based　semantic　segmentation　of　urban　MLS　point　clouds　has　achieved　significant　progress,　which　makes　it　possible　to　segment　tree　point　clouds.　However,　tree　segments　often　are　spatially　overlapping　with　varying　shapes　and　incompleteness　caused　by　occlusion,　which　makes　individual　tree　extraction　a　challenging　task.　In　this　paper,　we　propose　a　novel　top-down　approach　to　extract　individual　trees　from　urban　MLS　point　clouds.　Firstly,　a　semantic　segmentation　deep　network　is　applied　to　segment　tree　points　from　raw　urban　MLS　point　clouds,　and　then　the　segmented　tree　points　are　further　grouped　into　a　set　of　tree　clusters　using　Euclidean　distance　clustering.　Next,　a　pointwise　direction　embedding　deep　network　(PDE-net)　is　proposed　to　predict　the　direction　vectors　pointing　to　tree　centers　for　each　tree　cluster　to　enhance　the　boundaries　of　instance-level　trees.　After　that,　a　direction　aggregation-based　strategy　is　developed　to　detect　the　tree　centers　for　each　tree　cluster,　and　the　clusters　are　classified　into　single-tree　clusters　and　multi-tree　clusters　based　on　the　number　of　detected　tree　centers.　Finally,　the　single-tree　clusters　are　directly　extracted　as　individual　trees,　while　the　multi-tree　clusters　are　further　separated　into　instance-level　trees　based　on　our　proposed　accessible　region　growing　algorithm　combining　the　embedded　pointwise　directions　and　detected　tree　centers.　Four　MLS　point　clouds　collected　from　different　urban　scenes　were　used　to　evaluate　the　performance　of　the　proposed　method.　The　precision,　recall,　and　F-score　of　0.96,　0.94,　and　0.95,　respectively,　on　these　four　datasets　demonstrate　the　effectiveness　of　our　approach.　An　implementation　of　the　proposed　method　is　available　at:　https://github.　com/HiphonL/IndividualTreeExtraction.