Using　mobile　Light　Detection　and　Ranging　point　clouds　to　accomplish　road　scene　labeling　tasks　shows　promise　for　a　variety　of　applications.　Most　existing　methods　for　semantic　labeling　of　point　clouds　require　a　huge　number　of　fully　supervised　point　cloud　scenes,　where　each　point　needs　to　be　manually　annotated　with　a　specific　category.　Manually　annotating　each　point　in　point　cloud　scenes　is　labor　intensive　and　hinders　practical　usage　of　those　methods.　To　alleviate　such　a　huge　burden　of　manual　annotation,　in　this　paper,　we　introduce　an　active　learning　method　that　avoids　annotating　the　whole　point　cloud　scenes　by　iteratively　annotating　a　small　portion　of　unlabeled　supervoxels　and　creating　a　minimal　manually　annotated　training　set.　In　order　to　avoid　the　biased　sampling　existing　in　traditional　active　learning　methods,　a　neighbor-consistency　prior　is　exploited　to　select　the　potentially　misclassified　samples　into　the　training　set　to　improve　the　accuracy　of　the　statistical　model.　Furthermore,　lots　of　methods　only　consider　short-range　contextual　information　to　conduct　semantic　labeling　tasks,　but　ignore　the　long-range　contexts　among　local　variables.　In　this　paper,　we　use　a　higher　order　Markov　random　field　model　to　take　into　account　more　contexts　for　refining　the　labeling　results,　despite　of　lacking　fully　supervised　scenes.　Evaluations　on　three　data　sets　show　that　our　proposed　framework　achieves　a　high　accuracy　in　labeling　point　clouds　although　only　a　small　portion　of　labels　is　provided.　Moreover,　comparative　experiments　demonstrate　that　our　proposed　framework　is　superior　to　traditional　sampling　methods　and　exhibits　comparable　performance　to　those　fully　supervised　models.