Typhoons　and　hurricanes　cause　extensive　damage　to　coast　cities　annually,　demanding　urban　authorities　to　take　effective　actions　in　disaster　response　to　reduce　losses.　One　of　the　first　priority　in　disaster　response　is　to　identify　and　clear　road　obstacles,　such　as　fallen　trees　and　ponding　water,　and　restore　road　transportation　in　a　timely　manner　for　supply　and　rescue.　Traditionally,　identifying　road　obstacles　is　done　by　manual　investigation　and　reporting,　which　is　labor　intensive　and　time　consuming,　hindering　the　timely　restoration　of　transportation.　In　this　work,　we　propose　RADAR,　a　low-cost　and　real-time　approach　to　identify　road　obstacles　leveraging　large-scale　vehicle　trajectory　data　and　heterogeneous　road　environment　sensing　data.　First,　based　on　the　observation　that　road　obstacles　may　cause　abnormal　slow　motion　behaviors　of　vehicles　in　the　surrounding　road　segments,　we　propose　a　cluster　direct　robust　matrix　factorization　(CDRMF)　approach　to　detect　road　obstacles　by　identifying　the　collective　anomalies　of　slow　motion　behaviors　from　vehicle　trajectory　data.　Then,　we　classify　the　detected　road　obstacles　leveraging　the　correlated　spatial　and　temporal　features　extracted　from　various　road　environment　data,　including　satellite　images　and　meteorological　records.　To　address　the　challenges　of　heterogeneous　features　and　sparse　labels,　we　propose　a　semi-supervised　approach　combining　co-training　and　active　learning　(CORAL).　Real　experiments　on　Xiamen　City　show　that　our　approach　accurately　detects　and　classifies　the　road　obstacles　during　the　2016　typhoon　season　with　precision　and　recall　both　above　90%,　and　outperforms　the　state-of-the-art　baselines.　©　2018　ACM.