{"id":"W2412274631","doi":"10.5194/isprs-archives-xli-b3-221-2016","title":"STREET-SCENE TREE SEGMENTATION FROM MOBILE LASER SCANNING DATA","year":2016,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Government of Jiangsu Province; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Segmentation; Tree (set theory); Artificial intelligence; Computer science; Cluster analysis; Laser scanning; Computer vision; Scale-space segmentation; Point cloud; Region growing; Pattern recognition (psychology); Terrain; Mobile mapping; Voxel; Euclidean distance; Image segmentation; Geography; Mathematics; Laser; Cartography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001644573,0.0004753087,0.000414752,0.004108569,0.0004209994,0.0006404076,0.0004981022,0.0005668973,0.001768136],"category_scores_gemma":[0.0003965349,0.0002515959,0.0005150698,0.003364252,0.0001893087,0.0006685497,0.000389245,0.0003131724,0.00152618],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003290338,"about_ca_system_score_gemma":0.0005969449,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004615562,"about_ca_topic_score_gemma":0.01383957,"domain_scores_codex":[0.9997433,0.00002223452,0.00001349248,0.00005972358,0.00011144,0.0000498544],"domain_scores_gemma":[0.9996812,0.00006007884,0.00003509404,0.00004072689,0.0001647597,0.00001814349],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002763844,0.0002182202,0.02136611,0.000520108,0.0001361122,0.0007584173,0.0004498226,0.06135795,0.2003319,0.001803825,0.008954126,0.703827],"study_design_scores_gemma":[0.00002550712,0.0001731603,0.05490507,0.00006720077,0.00008343386,0.0009370667,0.0009165769,0.8199838,0.1022463,0.00325402,0.01732649,0.00008124115],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4714486,0.0007031615,0.5079361,0.0001743563,0.00006940067,0.0002940576,0.006364165,0.007370946,0.005639182],"genre_scores_gemma":[0.620333,0.0003900319,0.3657019,0.00004468712,0.0000379335,0.0001780698,0.01122163,0.0003013911,0.001791242],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004615562,"threshold_uncertainty_score":0.009177446,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01981889562227701,"score_gpt":0.2623827521672868,"score_spread":0.2425638565450098,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}