Water　leakage　is　one　of　the　common　tunnel　disease　problems.　Fast　and　accurate　detection　is　crucial　for　timely　management　of　leakage　disease.　This　paper　introduces　a　method　for　automatically　extracting　the　location　and　area　of　tunnel　leakage　from　3D　laser　scanning　point　clouds.　The　method　involves　three　key　steps:　segmenting　the　point　cloud　data　along　the　tunnel　axis　based　on　the　width　of　the　tube　sheet,　correcting　the　intensity　of　the　point　cloud　using　a　linear　intensity　correction　model,　and　binarizing　the　point　cloud　data　to　extract　the　leakage　area　and　calculate　its　area.　To　evaluate　the　performance　of　the　proposed　methodology,　water　leakage　detection　software　is　prepared　based　on　the　method,　and　tests　are　carried　out　in　the　urban　subway　tunnels　in　the　leakage　disease-producing　sections.　The　results　show　a　detection　accuracy　of　92　%　and　an　improvement　in　detection　efficiency　of　7-8　times.　The　method　proposed　can　be　used　to　quickly　and　accurately　extract　the　location　and　area　of　water　leakage　from　point　cloud　data.