We　study　a　multi-task　learning　framework　for　semantic　segmentation　in　Mobile　Laser　Scanning　(MLS)　point　clouds.　The　existing　methods　on　semantic　segmentation　of　point　cloud　rely　on　a　large　number　of　annotation　data.　However,　manually　annotating　data　is　time-consuming　and　laborious,　and　the　manually　annotation　efficiency　is　particularly　low.　To　alleviate　those　problems,　we　propose　to　exploit　a　multitask　learning　framework　to　reduce　the　large　demand　of　training　samples　for　implementing　semantic　labeling　of　point　clouds.　Specifically,　we　design　a　new　neural　network　containing　a　backbone　network　and　two　branching　networks,　which　accomplish　the　color　prediction　and　category　prediction,　respectively.　Color　prediction,　as　an　auxiliary　task,　can　be　easily　conducted　by　exploiting　the　color　information　of　each　3D　point　to　train　the　proposed　neural　network.　Here,　color　information　of　each　point　can　be　easily　generated　by　using　the　optical　images　obtained　by　the　cameras　equipped　in　the　MLS　system.　Once　the　training　procedure　of　color　prediction　is　completed,　we　only　use　a　small　portion　of　manually-annotated　points　to　fine-tune　the　branching　network　of　category　prediction　for　each　3D　point.　To　demonstrate　the　effectiveness　and　correctness　of　our　proposed　framework,　we　conducted　extensive　experiments　on　the　colorized　point　clouds　which　are　collected　by　a　RIEGL　VMX450　MLS　system.　The　experimental　results　show　the　proposed　approach　can　reach　96.04%.　OA　and　94.41%　mIoU　under　the　supervision　of　10%　annotation　data.