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The?point?cloud?obtained?by?airborne?lidar?has?the?characteristics?of?low?density,?uneven?distribution,?unclear?branch?structure,?etc.?The?dynamic?deviation?of?data?features?in?the?dynamic?scanning?process?is?very?small,?and?it?is?unable?to?extract?effective?data?denoising?features.?Therefore,?a?real-time?classification?method?of?airborne?lidar?point?cloud?data?under?the?constraint?of?skewness?features?is?proposed.?In?this?method,?the?large?capacity?real-time?data?of?point?cloud?obtained?by?scanning?is?introduced?into?the?normal?distribution,?and?the?key?metric?skewness?feature?measuring?the?symmetry?of?the?normal?distribution?is?used?as?the?dynamic?feature?boundary?constraint?to?complete?the?data?filtering;?The?point?cloud?features?of?airborne?lidar?are?extracted,?from?which?high-quality?features?are?selected?to?build?a?SVM?classifier.?The?final?classification?result?is?the?result?of?point?cloud?high-capacity?data?training.?The?experimental?results?show?that?the?proposed?method?has?high?accuracy?and?efficiency.